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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

Software and model selection challenges inmeta-analysis

MetaEasy, model assumptions, homogeneity and IPD

Evangelos Kontopantelis

Centre for Health InformaticsInstitute of Population Health

University of Manchester

Attikon HospitalAthens, 2 June 2014

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Timeline

Efforts to pool results from individual studies back as far as1904The first attempt that assessed a therapeutic interventionwas published in 1955In 1976 Glass first used the term to describe a "statisticalanalysis of a large collection of analysis results fromindividual studies for the purpose of integrating thefindings"Relatively young and dynamic field of research, reflectingimportance of MA and potential for conclusive answers

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Meta-analysing reported study results

A two-stage processthe relevant summary effect statistics are extracted frompublished papers on the included studiesthese are then combined into an overall effect estimateusing a suitable meta-analysis model

However, problems often arisepapers do not report all the statistical information requiredas inputpapers report a statistic other than the effect size whichneeds to be transformed with a loss of precisiona study might be too different to be included (populationclinically heterogeneous)

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Individual Patient DataIPD

These problems can be avoided when IPD from eachstudy are available

outcomes can be easily standardisedclinical heterogeneity can be addressed with subgroupanalyses and patient-level covariate controlling

Can be analysed in a single- or two-stage processmixed-effects regression models can be used to combineinformation across studies in a single stagethis is currently the best approach, with the two-stagemethod being at best equivalent in certain scenarios

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Meta-analyses on the rise

Meta-analysis studiesare used more and moresince they seem to be auseful ‘summary’ tool.However critics arguethat one cannot combinestudies when they aretoo diverse(heterogeneous).

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Outline

1 Meta-analysis overviewThe heterogeneity issue

2 Two-stage meta-analysis

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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Grou

p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

When Robin ruins the party...

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Heterogeneitytread lightly!

When the effect of the intervention varies significantly fromone study to anotherIt can be attributed to clinical and/or methodologicaldiversity

Clinical: variability that arises from different populations,interventions, outcomes and follow-up timesMethodological: relates to differences in trial design andquality

Detecting quantifying and dealing with heterogeneity canbe very hard

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Absence of heterogeneity

Assumes that thetrue effects of thestudies are allequal anddeviations occurbecause ofimprecision ofresultsAnalysed with thefixed-effectsmethod

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Presence of heterogeneity

Assumes thatthere is variation inthe size of the trueeffect amongstudies (in additionto the imprecisionof results)Analysed withrandom-effectsmethods

Kontopantelis Software and model selection challenges in meta-analysis

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p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Random or fixed?two ‘schools’ of thought

Fixed-effect (FE)‘what is the average result of trials conducted to date’?assumption-free

Random-effects (RE)‘what is the true treatment effect’?various assumptions

normally distributed trial effectsvarying treatment effect across populations although findingslimited since based on observed studies only

more conservative; findings potentially more generalisable

Researchers reassured when τ2 = 0FE often used when low heterogeneity detected

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

The heterogeneity issue

Not simple

Start(sort of)

Outcome(s) continuous

Inverse Variance weighting methods (IV)

Yes

Fixed-effect by conviction

Fixed-effect IV model

Yes

No

Detected heterogeneity

No Random-effects IV model

DL VC ML

REMLPL

Yes

Outcome(s) dichotomousNo

Maentel-Haenszel methods (MH)

Fixed-effect by conviction

Fixed-effect MH true model

YesDetected

heterogeneity

NoCombining dichotomous

and continuous outcomes

Transform dichotomous

outsomes to SMD

Feeling adventurous?

Yes

Yes! No!Rare events

Very rare events?

Estimate heterogeneity (τ2)No

No

Random-effects MH-IV hybrid model

Yes

Peto methods (P)

Fixed-effect Peto true model

YesNo

Outcome(s) time-to-eventNo

Fixed-effect Peto O-E true model

Yes

Bayesian?

No

τ2 est

BP

MVaMVb

Yes

Random-effects IV model

DL

τ2 estimation

DL DL2 DLb VC

VC2ML REML PL

Non-zero prior

Yes

τ2 est

B0

No

Kontopantelis Software and model selection challenges in meta-analysis

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p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Challenges with two-stage meta-analysis

Heterogeneity is common and the fixed-effect model isunder fireMethods are asymptotic: accuracy improves as studiesincrease. But what if we only have a handful, as is usuallythe case?Almost all random-effects models (except ProfileLikelihood) do not take into account the uncertainty in τ2.Is this, practically, a problem?DerSimonian-Laird is the most common method ofanalysis, since it is easy to implement and widely available,but is it the best?

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Challenges with two-stage meta-analysis...continued

Can be difficult to organise since...outcomes likely to have been disseminated using a varietyof statistical parametersappropriate transformations to a common format requiredtedious task, requiring at least some statistical adeptness

Parametric random-effects models assume that both theeffects and errors are normally distributed. Are methodsrobust?Sometimes heterogeneity is estimated to be zero,especially when the number of studies is small. Goodnews?

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

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7 (259%) 18 (667%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Outline

1 Meta-analysis overview

2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Organising

Data initially collected using data extraction formsA spreadsheet is the next logical step to summarise thereported study outcomes and identify missing dataSince in most cases MS Excel will be used we developedan add-in that can help with most processes involved inmeta-analysisMore useful when the need to combine differently reportedoutcomes arises

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

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106 (50%) 101 (476%)

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Nostrud: N Y

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

What it can do

Help with the data collection using pre-formattedworksheets.Its unique feature, which can be supplementary to othermeta-analysis software, is implementation of methods forcalculating effect sizes (& SEs) from different input typesFor each outcome of each study...

it identifies which methods can be usedcalculates an effect size and its standard errorselects the most precise method for each outcome

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

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106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

What it can do...continued

Creates a forest plot that summarises all the outcomes,organised by studyUses a variety of standard and advanced meta-analysismethods to calculate an overall effect

a variety of options is available for selecting whichoutcome(s) are to be meta-analysed from each study

Plots the results in a second forest plotReports a variety of heterogeneity measures, includingCochran’s Q, I2, HM

2 and τ2 (and its estimated confidenceinterval under the Profile Likelihood method)

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Advantages

Free (provided Microsoft Excel is available)Easy to use and time savingExtracted data from each study are easily accessible, canbe quickly edited or corrected and analysis repeatedChoice of many meta-analysis models, including someadvanced methods not currently available in other softwarepackages (e.g. Permutations, Profile Likelihood, REML)Unique forest plot that allows multiple outcomes per studyEffect sizes and standard errors can be exported for use inother meta-analysis software packages

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

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OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

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106 (50%) 101 (476%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Installing

Latest version available from www.statanalysis.co.ukCompatible with Excel 2003, 2007 and 2010Manual provided but also described in:

Kontopantelis E and Reeves DMetaEasy: A Meta-Analysis Add-In for Microsoft ExcelJournal of Statistical Software, 30(7):1-25, 2009

Play video clip

Kontopantelis Software and model selection challenges in meta-analysis

Heal

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Outline

1 Meta-analysis overview

2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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rimar

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p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Stata implementation

MetaEasy methods implemented in Stata under:metaeff, which uses the different study input to provideeffect sizes and SEsmetaan, which meta-analyses the study effects with afixed-effect or one of five available random-effects models

To install, type in Stata:ssc install <command name>help <command name>

Described in:Kontopantelis E and Reeves Dmetaan: Random-effects meta-analysisThe Stata Journal, 10(3):395-407, 2010

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Outline

1 Meta-analysis overview

2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

Heal

th S

cien

ces P

rimar

y Ca

re R

esea

rch

Grou

p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Many random-effects methodswhich to use?

DerSimonian-Laird (DL): Moment-based estimator of bothwithin and between-study varianceMaximum Likelihood (ML): Improves the variance estimateusing iterationRestricted Maximum Likelihood (REML): an ML variationthat uses a likelihood function calculated from atransformed set of dataProfile Likelihood (PL): A more advanced version of MLthat uses nested iterations for convergingPermutations method (PE): Simulates the distribution ofthe overall effect using the observed data

Kontopantelis Software and model selection challenges in meta-analysis

Heal

th S

cien

ces P

rimar

y Ca

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esea

rch

Grou

p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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[Graphic title]

RESULTS

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Performance evaluationour approach

Simulated various distributions for the trueeffects:

NormalSkew-NormalUniformBimodal

Created datasets of 10,000meta-analyses for various numbers ofstudies and different degrees ofheterogeneity, for each distribution

Kontopantelis Software and model selection challenges in meta-analysis

Heal

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cien

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rimar

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rch

Grou

p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Add key point.]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Performance evaluationour approach

Compared all methods in terms of:Coverage, the rate of true negatives when the overall trueeffect is zeroPower, the rate of true positives when the true overall effectis non-zero

Kontopantelis Software and model selection challenges in meta-analysis

Heal

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p

[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Coverage by methodLarge heterogeneity across various between-study variance distributions

0.50

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H=2.78 − I=64%FE − Coverage

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Zero variance Normal Skew−normal

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H=2.78 − I=64%PE − Coverage

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Power by methodLarge heterogeneity across various between-study variance distributions

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Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

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1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Which method then?

Within any method, consistent results across alldistribution typesRobust against even severe violations of normalityPL has a ‘reasonable’ coverage in most situations,especially for moderate and large heterogeneiryREML and DL perform similarly and better than PL onlywhen heterogeneity is low (I2 < 15%)Described in:

Kontopantelis E and Reeves DPerformance of statistical methods for meta-analysis whentrue study effects are non-normally distributedStat Methods Med Res, published online Dec 9 2010

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Outline

1 Meta-analysis overview

2 Two-stage meta-analysisthe MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

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26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Cochrane Database for Systematic Reviews

Richest resource of meta-analyses in the worldFifty-four active groups responsible for organising, advisingon and publishing systematic reviewsAuthors obliged to use RevMan and submit the data andanalyses file along with the review, contributing to thecreation of a vast data resourceRevMan offers quite a few fixed-effect choices but only theDerSimonian-Laird random-effects method has beenimplemented to quantify and account for heterogeneity

hidden data

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

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Lorem

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5 (24%)

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Nostrud: N Y

Unknown

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1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

‘Real’ DataCochrane Database for Systematic Reviews

Python code to crawl Wiley website for RevMan filesDownloaded 3,845 relevant RevMan files (of 3,984available in Aug 2012) and imported in StataEach file a systematic reviewWithin each file, various research questions might havebeen posed

investigated across various relevant outcomes?variability in intervention or outcome?

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

‘Real’ DataCochrane Database for Systematic Reviews

Cochrane database

CD000006

Group: Pregnancy and Childbirth

Review name: Absorbable suture

materials for primary repair of episiotomy and

second degree tears

Meta-analysis 1Synthetic sutures

versus catgut

Meta-analysis 2Fast-absorbing synthetic versus

standard absorbable synthetic material

Meta-analysis 3Glycerol impregnated catgut (softgut)

versus chromic catgut

Meta-analysis 4Monofilament versus standard

polyglycolic sutures

Outcome 1.1Short-term pain: pain at day 3 or less

(women experiencing any pain)

Subgroup 1.1.1Standard synthetic; k=9

Subgroup 1.1.2Fast absorbing; k=1

Outcome 1.9Dyspareunia - at 3 months

postpartumSubgroup 1.9.1

Standard synthetic; k=5

Subgroup 1.9.2Fast absorbing; k=1

Main 1.9.0k=6

Main 1.1.0k=10

Outcome 2.1Short-term pain: at 3 days or less

Main 2.1.0k=3

Outcome 2.11Maternal satisfaction: satisfied with

repair at 12 months

Main 2.11.0k=1

Outcome 3.1Short-term pain: pain at 3 days or

less

Main 3.1.0k=1

Outcome 3.8Dyspareunia at 6 - 12 months

Main 3.8.0k=1

Outcome 4.1Short-term pain: mean pain scores at

3 days

Main 4.1.0k=1

Outcome 4.4Wound problems at 8 - 12 weeks:

women seeking professional help for problem with perineal repair

Main 4.4.0k=1

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

The questions

Investigate the potential bias when assuming homogeneityAssess method performance in relation to heterogeneityestimates in simulationsExamine the distribution of measured heterogeneity in allCochrane meta-analysesPresent details on the number of meta-analysed studiesand model selectionAssess the sensitivity of results and conclusions usingalternative models

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Which method?simulation results

Bayesian methods (assuming a prior heterogeneity value)did very well for very small meta-analysesOur suggested variant of the non-parametric bootstrap forthe standard estimate, DLb, seemed best method overall

especially in detecting heterogeneitywhich appears to be a big problem: DL failed to detect highτ2 for over 50% of small meta-analyses

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Meta-analyses numbers

Of the 3,845 files 2,801 had identified relevant studies andcontained any data98,615 analyses extracted 57,397 of which meta-analyses

32,005 were overall meta-analyses25,392 were subgroup meta-analyses

Estimation of an overall effectPeto method in 4,340 (7.6%)Mantel-Haenszel in 33,184 (57.8%)Inverse variance in 19,873 (34.6%)random-effects more prevalent in inverse variance methodsand larger meta-analyses

34% of meta-analyses on 2 studies (53% k ≤ 3)!

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Meta-analyses by Cochrane group

22

Figures Figure 1: All meta-analyses, including single-study and subgroup meta-analyses

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Single Study Fixed-effect model (by choice or necessity) Random-effects model

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

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OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Meta-analyses by method choice

23

Figure 2: Model selection by number of available studies (and % of random-effects meta-analyses)*

*note that in many cases fixed-effect models were used when heterogeneity was detected

Figure 3: Comparison of zero between-study variance estimates rates in the Cochrane library data and in simulations, using the DerSimonian-Laird method*

*Normal distribution of the effects assumed in the simulations (more extreme distributions produced similar results).

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ABSTRACT TITLE: [Add text here.]

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Comparing Cochrane data with simulated

To assess the validity of a homogeneity assumption wecompared the percentage of zero estimates, in real andsimulated dataCalculated DL heterogeneity estimate for all Cochranemeta-analysesPercentage of zero estimates equivalent to moderate-highheterogeneity simulated dataSuggests that mean true heterogeneity is higher thangenerally assumed but fails to be detected; especially forsmall meta-analyses

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Comparing Cochrane data with simulated

23

Figure 2: Model selection by number of available studies (and % of random-effects meta-analyses)*

*note that in many case fixed-effect models were used when heterogeneity was detected

Figure 3: Comparison of zero between-study variance estimates rates in the Cochrane library data and in simulations, using the DerSimonian-Laird method*

*Normal distribution of the effects assumed in the simulations (more extreme distributions produced similar results).

21%

27%

31% 37%

41% 51%

15%

19%

22%

22%

27% 30%

0

2000

4000

6000

8000

10000

12000

2 3 4 5 6-9 10+Number of Studies in meta-analysis

Peto (FE) Inverse Variance (FE) Inverse Variance (RE) Mantel-Haenszel (FE) Mantel-Haenszel (RE)

0

10

20

30

40

50

60

70

80

90

100

2 3 4 5 10 20

% o

f zer

o τ^

2 es

timat

es w

ith D

erSi

mon

ian-

Laird

Number of studies in meta-analyis

Observed

true I^2=15%

true I^2=35%

true I^2=64%

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Add key point.]

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[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Reanalysing the Cochrane data

We applied all methods to all 57,397 meta-analyses toassess heterogeneity distributions and the sensitivity of theresults and conclusionsFor simplicity discuss differences between standardmethods and DLb; not a perfect method but one thatperformed well overallAs in simulations, DLb identifies more heterogeneousmeta-analyses; τ2

DL = 0 for 50.5% & τ2DLb = 0 for 31.2%

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Distributions for τ2

050

010

0015

0020

00

# of

met

a-an

alys

es

0 .1 .2 .3 .4 .5

t2 estimate

Zero est(%): DL=44.9, DLb=29.6, VC=48.9 REML=45.4 ML=62.2, B0=49.2, VC2=44.3, DL2=45.3Non-convergence(%): ML=0.7, REML=1.4.

Inverse Variance

010

0020

0030

0040

0050

00

# of

met

a-an

alys

es

0 .1 .2 .3 .4 .5

t2 estimate

Zero est(%): DL=54.2, DLb=32.7, VC=58.8 REML=55.6 ML=75.0, B0=59.6, VC2=53.9, DL2=55.5Non-convergence(%): ML=1.3, REML=1.9.

Mantel-Haenszel

020

040

060

0

# of

met

a-an

alys

es

0 .1 .2 .3 .4 .5

t2 estimate

Zero est(%): DL=50.8, DLb=27.3, VC=54.2 REML=51.4 ML=70.0, B0=54.8, VC2=49.6, DL2=51.0Non-convergence(%): ML=0.6, REML=1.0.

Peto & O-E

020

0040

0060

0080

00

# of

met

a-an

alys

es

0 .1 .2 .3 .4 .5

t2 estimate

Zero est(%): DL=50.7, DLb=31.2, VC=55.0 REML=51.7 ML=70.2, B0=55.6, VC2=50.2, DL2=51.6Non-convergence(%): ML=1.0, REML=1.6.

all methods

non-zero estimates only

DL DLb VC ML

REML B0 VC2 DL2

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

Changes in results and conclusionse.g. inverse variance analyses

RevMan DerSimonian-Laird Random-effects method says

heterogeneity is present

Analysis with bootstrap DL rarely changes conclusions (although higher heterogeneity estimates and found in around 20% more

meta-analysis

Conclusions change for:0.9% of analyses

No

Estimated heterogeneity ‘ignored’ by authors and a

fixed-effect model is chosenYes

Analysis with bootstrap DL rarely changes conclusions

Conclusions change for:2.4% of analyses

No

Analysis with bootstrap DL makes a difference in 1 in 5 analyses (as would analysis with standard DL

but to a smaller extent)

Conclusions change for:19.1% of analyses

Yes

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

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[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

the MetaEasy add-inmetaeff & metaanMethods and performanceCochrane database

FindingsCochrane database analysis

Detecting and accurately estimating heterogeneity in smallMAs is very difficult; yet for 53% of Cochrane MAs, k ≤ 3Estimates of zero heterogeneity should be a concernBootstrapped DL leads to a small improvement butproblem largely remains, especially for very small MAsAssume high levels of heterogeneity in sensitivity?Caution against ignoring heterogeneity when detectedDiscussed in:

Kontopantelis E, Springate DA and Reeves DA Re-Analysis of the Cochrane Library Data: The Dangersof Unobserved Heterogeneity in Meta-AnalysesPLOS ONE, 10.1371/journal.pone.0069930, 26 July 2013

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Forest plot

One advantage of two-stage meta-analysis is the ability toconvey information graphically through a forest plot

study effects available after the first stage of the process,and can be used to demonstrate the relative strength of theintervention in each study and across allinformative, easy to follow and particularly useful forreaders with little or no methodological experiencekey feature of meta-analysis and always presented whentwo-stage meta-analyses are performed

In one-stage meta-analysis, only the overall effect iscalculated and creating a forest-plot is not straightforward

Enter ipdforest

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysisA practical guideipdforestExamplesPower

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

The hypothetical study

Individual patient data from randomised controlled trialsFor each trial we have

a binary control/intervention membership variablebaseline and follow-up data for the continuous outcomecovariates

Assume measurements consistent across trials andstandardisation is not requiredWe will explore linear random-effects models with thextmixed command; application to the logistic case usingxtmelogit should be straightforwardIn the models that follow, in general, we denote fixedeffects with ‘γ’s and random effects with ‘β’s

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model 1fixed common intercept; random treatment effect; fixed effect for baseline

Yi j = γ0 + β1jgroupi j + γ2Ybi j + εi j εi j ∼ N(0, σ2j )

β1j = γ1 + u1j u1j ∼ N(0, τ12)

i : the patientj : the trialYi j : the outcomeγ0: fixed common interceptβ1j : random treatmenteffect for trial jγ1: mean treatment effectgroupi j : group membership

γ2: fixed baseline effectYbi j : baseline scoreu1j : random treatmenteffect for trial jτ1

2: between trial varianceεi j : error termσ2

j : within trial variance fortrial j

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model 1fixed common intercept; random treatment effect; fixed effect for baseline

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model basicsCommon fixed-effect

05

101520253035

1 2 3 4

Study

Fixed-effectcommon intercept

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model basicsRandom-effect

05

101520253035

1 2 3 4

Study

Random-effectsintercept

Random-effectsintercept level

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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[Add key point.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model basicsTrial specific fixed-effect

05

101520253035

1 2 3 4

Study

Fixed-effect trial-specific intercept

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

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[Add title, if necessary.]

[Add key point.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model 1fixed common intercept; random treatment effect; fixed effect for baseline

Possibly the simplest approachIn Stata it can be expressed as

xtmixed Y i.group Yb || studyid:group,nocons

05

101520253035

1 2 3 4

Study

Fixed-effectcommon intercept

05

101520253035

1 2 3 4

Study

Random-effectstreatment

Random-effectstreatmentmean

05

101520253035

1 2 3 4

Study

Fixed-effectcommon covariate

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model 2fixed trial intercepts; random treatment effect; fixed trial effects for baseline

Common intercept & fixed baseline difficult to justifyA more accepted model:

xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4|| studyid:group, nocons

05

101520253035

1 2 3 4

Study

Fixed-effect trial-specific intercept

05

101520253035

1 2 3 4

Study

Random-effectstreatment

Random-effectstreatmentmean

05

101520253035

1 2 3 4

Study

Fixed-effect trial-specific covariate

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Add key point.]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model 3random trial intercept; random treatment effect; fixed trial-specific effects for baseline

Another possibility, althought contentious, is to assume trialintercepts are random (e.g. multi-centre trial):

xtmixed Y i.group Yb1 Yb2 Yb3 Yb4 ||studyid:group, cov(uns)

05

101520253035

1 2 3 4

Study

Random-effectsintercept

Random-effectsintercept level

05

101520253035

1 2 3 4

Study

Random-effectstreatment

Random-effectstreatmentmean

05

101520253035

1 2 3 4

Study

Fixed-effectcommon covariate

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Model 4random trial intercept; random treatment effect; random effects for baseline

The baseline could also have been modelled as arandom-effect:

xtmixed Y i.group Yb || studyid:group Yb,cov(uns)

05

101520253035

1 2 3 4

Study

Random-effectsintercept

Random-effectsintercept level

05

101520253035

1 2 3 4

Study

Random-effectstreatment

Random-effectstreatmentmean

05

101520253035

1 2 3 4

Study

Random-effectscovariate

Random-effectscovariate level

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Interactions and covariates

One of the major advantages of IPD meta-analysesA covariate or an interaction term can be modelled as afixed or random effectIn Stata expressed as:

xtmixed Y i.group i.studyid Yb1 Yb2 Yb3 Yb4age i.group#c.age || studyid:group, nocons

If modelled as a random effect, non-convergence issuesmore likely to be encountered

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add title, if necessary.]

[Add key point.]

[Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Add key point.]

[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

[Add key point.]

[Sub-bullet] [Sub-bullet]

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[Add key point.]

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[Sub-bullet] [Sub-bullet]

Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysisA practical guideipdforestExamplesPower

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

Heal

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

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0 1 (37%) 1 (37%)

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Lorem

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

General

ipdforest is issued following an IPD meta-analysis thatuses mixed effects two-level regression, with patientsnested within trials and a

linear model (xtmixed)logistic model (xtmelogit)

Provides a meta-analysis summary table and a forest plotTrial effects are calculated within ipdforest

Can calculate and report both main and interaction effectsOverall effect(s) & variance estimates extracted frompreceding regressionCalculates and reports heterogeneity

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

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26 (963%) 0

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysisA practical guideipdforestExamplesPower

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

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Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

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26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Depression intervention

We apply the ipdforest command to a dataset of 4depression intervention trialsComplete information in terms of age, gender,control/intervention group membership, continuousoutcome baseline and endpoint values for 518 patientsFake author names and generated random continuous &binary outcome variables, while keeping the covariates attheir actual valuesIntroduced correlation between baseline and endpointscores and between-trial variabilityLogistic IPD meta-analysis, followed by ipdforest

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

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26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Dataset

. use ipdforest_example.dta,

. describe

Contains data from ipdforest_example.dtaobs: 518vars: 17 6 Feb 2012 11:14size: 20,202

storage display valuevariable name type format label variable label

studyid byte %22.0g stid Study identifierpatid int %8.0g Patient identifiergroup byte %20.0g grplbl Intervention/control groupsex byte %10.0g sexlbl Genderage float %10.0g Age in yearsdepB byte %9.0g Binary outcome, endpointdepBbas byte %9.0g Binary outcome, baselinedepBbas1 byte %9.0g Bin outcome baseline, trial 1depBbas2 byte %9.0g Bin outcome baseline, trial 2depBbas5 byte %9.0g Bin outcome baseline, trial 5depBbas9 byte %9.0g Bin outcome baseline, trial 9depC float %9.0g Continuous outcome, endpointdepCbas float %9.0g Continuous outcome, baselinedepCbas1 float %9.0g Cont outcome baseline, trial 1depCbas2 float %9.0g Cont outcome baseline, trial 2depCbas5 float %9.0g Cont outcome baseline, trial 5depCbas9 float %9.0g Cont outcome baseline, trial 9

Sorted by: studyid patid

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

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CONCLUSIONS

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

ME logistic regression model - continuous interactionfixed trial intercepts; fixed trial effects for baseline; random treatment and age effects

. xtmelogit depB group agec sex i.studyid depBbas1 depBbas2 depBbas5 depBbas9 i> .group#c.agec || studyid:group agec, var nocons or

Mixed-effects logistic regression Number of obs = 518Group variable: studyid Number of groups = 4

Obs per group: min = 42avg = 129.5max = 214

Integration points = 7 Wald chi2(11) = 42.06Log likelihood = -326.55747 Prob > chi2 = 0.0000

depB Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]

group 1.840804 .3666167 3.06 0.002 1.245894 2.71978agec .9867902 .0119059 -1.10 0.270 .9637288 1.010403sex .7117592 .1540753 -1.57 0.116 .4656639 1.087912

studyid2 1.050007 .5725516 0.09 0.929 .3606166 3.0573035 .8014551 .5894511 -0.30 0.763 .189601 3.3877999 1.281413 .6886057 0.46 0.644 .4469619 3.673735

depBbas1 3.152908 1.495281 2.42 0.015 1.244587 7.987253depBbas2 4.480302 1.863908 3.60 0.000 1.982385 10.12574depBbas5 2.387336 1.722993 1.21 0.228 .5802064 9.823007depBbas9 1.881203 .7086507 1.68 0.093 .8990569 3.936262

group#c.agec1 1.011776 .0163748 0.72 0.469 .9801858 1.044385

_cons .5533714 .2398342 -1.37 0.172 .2366472 1.293993

Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]

studyid: Independentvar(group) 8.86e-21 2.43e-11 0 .var(agec) 5.99e-18 4.40e-11 0 .

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

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[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

ipdforestmodelling main effect and interaction

. ipdforest group, fe(sex) re(agec) ia(agec) or

One-stage meta-analysis results using xtmelogit (ML method) and ipdforestMain effect (group)

Study Effect [95% Conf. Interval] % Weight

Hart 2005 2.118 0.942 4.765 19.88Richards 2004 2.722 1.336 5.545 30.69Silva 2008 2.690 0.748 9.676 8.11Kompany 2009 1.895 0.969 3.707 41.31

Overall effect 1.841 1.246 2.720 100.00

One-stage meta-analysis results using xtmelogit (ML method) and ipdforestInteraction effect (group x agec)

Study Effect [95% Conf. Interval] % Weight

Hart 2005 0.972 0.901 1.049 19.88Richards 2004 0.995 0.937 1.055 30.69Silva 2008 0.987 0.888 1.098 8.11Kompany 2009 1.077 1.015 1.144 41.31

Overall effect 1.012 0.980 1.044 100.00

Heterogeneity Measures

value [95% Conf. Interval]

I^2 (%) .H^2 .tau^2 est 0.000 0.000 .

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

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7 (259%) 18 (667%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Forest plotsmain effect and interaction

Overall effect

Kompany 2009

Silva 2008

Richards 2004

Hart 2005

Stu

dies

0 2 3 4 5 6 7 8 9 101Effect sizes and CIs (ORs)

Main effect (group)

Overall effect

Kompany 2009

Silva 2008

Richards 2004

Hart 2005

Stu

dies

0 .2 .4 .6 .8 1.2 1.41Effect sizes and CIs (ORs)

Interaction effect (group x agec)

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

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Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysisA practical guideipdforestExamplesPower

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

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RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

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26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

Power calculationsto detect a moderator effect

The best approach is through simulationsAs always, numerous assumptions need to be made

effect sizes (main and interaction)exposure and covariate distributionscorrelation between variableswithin-study (error) variancebetween-study (error) variance - ICC

Generate 1000s of data sets using the assumed model(s)Estimate what % of these give a significant p-value for theinteractionCan be trial and error till desired power level achieved

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

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Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

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Lorem

106 (50%) 101 (476%)

5 (24%)

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Nostrud: N Y

Unknown

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26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

A practical guideipdforestExamplesPower

ipdpower

Currently under reviewStata program that allows for:

continuous, binary or count outcomevarious random effects as discussed in ipdforestvarious distributional assumptions (e.g. skew-normaloutcome)main effects and interactions (moderators effects)

Reports power, coverage, effect estimates andheterogeneity estimates

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

Outline

1 Meta-analysis overview

2 Two-stage meta-analysis

3 One-stage meta-analysis

4 Summary

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

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For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

Two-stage meta-analysis

MetaEasy can help organise meta-analysis and can bevery useful if need to combine continuous and binaryoutcomes.Methods implemented in Stata under metaeff and metaanZero heterogeneity estimate is a reason to worry;heterogeneity might be there but we cannot measure oraccount for in the modelIf detected, even if very small, use a random-effects modelfor generalisability?All methods likely to miss or underestimate heterogeneity

use sensitivity analysis approach?

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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Meta-analysis overviewTwo-stage meta-analysisOne-stage meta-analysis

Summary

One-stage meta-analysis

A few different approaches exist for conducting one-stageIPD meta-analysisStata can cope through the xtmixed and the xtmelogitcommandsThe ipdforest command aims to help meta-analysts

calculate trial effectsdisplay results in standard meta-analysis tablesproduce familiar and ‘expected’ forest-plots

The easiest way to calculate power to detect complexeffects is through simulations

see ipdpower, under review

Kontopantelis Software and model selection challenges in meta-analysis

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[Poster title]

ABSTRACT TITLE: [Add text here.]

BACKGROUND: [Add text here.]

OBJECTIVE: [Add text here.]

METHODS: [Add text here.]

RESULTS: [Add text here.]

CONCLUSIONS: [Add text here.]

BACKGROUND [Add title, if necessary.]

Label One

Label Two

Label Three

Label Four

[Replace the following names and titles with those of the actual contributors: Helge Hoeing, PhD1; Carol Philips, PhD2; Jonathan Haas, RN, BSN, MHA3, and Kimberly B. Zimmerman, MD4 1[Add affiliation for first contributor], 2[Add affiliation for second contributor], 3[Add affiliation for third contributor], 4[Add affiliation for fourth contributor]

OBJECTIVE

[Repeat objective from above.]

METHODS

[Add title, if necessary.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add key point.] [Add description of key point.]

[Add title, if necessary.]

[Add key point.] [Sub-bullet] [Sub-bullet]

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RESULTS

[Add title, if necessary.]

[Add key point.] [Add key point.] [Add key point.] [Add key point.] [Add key point.]

CONCLUSIONS

[Add text as bulleted list or a paragraph.] [Add key point.]

[Add key point.]

[Add key point.]

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[Graphic title]

RESULTS

[Graphic title] [Graphic title]

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[Replace, move, resize, or delete graphic, as necessary.] [Replace, move, resize, or delete graphic, as necessary.]

For additional information please contact: [Name] [Department] [Institution or organization] [E-mail address]

Excepteur Sint Lkl

(n=212) Controls

(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

Unknown

79 (373%) 121 (571%)

2 (09%) 0

1 (05%) 9 (42%)

7 (259%) 18 (667%)

0 1 (37%) 1 (37%)

0

Proident F

Lorem

106 (50%) 101 (476%)

5 (24%)

17 (63%) 10 (37%)

Nostrud: N Y

Unknown

172 (811%) 22 (104%) 18 (85%)

26 (963%) 0

1 (37%)

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AppendixThank you!References

Comments, suggestions:e.kontopantelis@manchester.ac.uk

Kontopantelis Software and model selection challenges in meta-analysis

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(n=27)

Lorum Wt (kg) 18 (SD 10) 29 (SD 07)

Ipsum (wk) 31 (SD 5) 37 (SD 2)

Irure: B W H HB O

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79 (373%) 121 (571%)

2 (09%) 0

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AppendixThank you!References

JSS Journal of Statistical SoftwareApril 2009, Volume 30, Issue 7. http://www.jstatsoft.org/

MetaEasy: A Meta-Analysis Add-In for Microsoft

Excel

Evangelos KontopantelisNational Primary Care Research

and Development Centre

David ReevesNational Primary Care Research

and Development Centre

Abstract

Meta-analysis is a statistical methodology that combines or integrates the results ofseveral independent clinical trials considered by the analyst to be ‘combinable’ (Huque1988). However, completeness and user-friendliness are uncommon both in specialisedmeta-analysis software packages and in mainstream statistical packages that have to relyon user-written commands. We implemented the meta-analysis methodology in an Mi-crosoft Excel add-in which is freely available and incorporates more meta-analysis models(including the iterative maximum likelihood and profile likelihood) than are usually avail-able, while paying particular attention to the user-friendliness of the package.

Keywords: meta-analysis, forest plot, Excel, VBA, maximum likelihood, profile likelihood.

1. Introduction

Meta-analysis can be defined as the statistical analysis of a large collection of analysis resultsfrom individual studies - usually Randomised Controlled Trials (RCTs) - for the purpose ofintegrating the findings (Glass 1976). Although the debate regarding the quality and ap-plication caveats of the method is ongoing (Egger and Smith 1997; Bailar 1997), a Medline(http://medline.cos.com/) search by the authors reveals that the number of meta-analysisstudies published in peer-reviewed journals seems to be growing exponentially (Figure 1).Published meta-analysis studies (search criterion: Publication Type=meta-analysis) haverisen from 274 in 1990 to 2138 in 2005, while published work that is either a meta-analysisor deals with meta-analysis issues (search criterion: Keyword=meta-analysis) has increasedfrom 329 to 3350, in the same period.

A major issue in meta-analysis is the almost inevitable clinical or methodological heterogene-ity among the combined studies (Eysenck 1994). If the study results differ greatly (large

A Re-Analysis of the Cochrane Library Data: The Dangersof Unobserved Heterogeneity in Meta-AnalysesEvangelos Kontopantelis1,2,3*, David A. Springate1,2, David Reeves1,2

1 Centre for Primary Care, NIHR School for Primary Care Research, Institute of Population Health, University of Manchester, Manchester, United Kingdom, 2 Centre for

Biostatistics, Institute of Population Health, University of Manchester, Manchester, United Kingdom, 3 Centre for Health Informatics, Institute of Population Health,

University of Manchester, Manchester, United Kingdom

Abstract

Background: Heterogeneity has a key role in meta-analysis methods and can greatly affect conclusions. However, true levelsof heterogeneity are unknown and often researchers assume homogeneity. We aim to: a) investigate the prevalence ofunobserved heterogeneity and the validity of the assumption of homogeneity; b) assess the performance of various meta-analysis methods; c) apply the findings to published meta-analyses.

Methods and Findings: We accessed 57,397 meta-analyses, available in the Cochrane Library in August 2012. Usingsimulated data we assessed the performance of various meta-analysis methods in different scenarios. The prevalence of azero heterogeneity estimate in the simulated scenarios was compared with that in the Cochrane data, to estimate thedegree of unobserved heterogeneity in the latter. We re-analysed all meta-analyses using all methods and assessed thesensitivity of the statistical conclusions. Levels of unobserved heterogeneity in the Cochrane data appeared to be high,especially for small meta-analyses. A bootstrapped version of the DerSimonian-Laird approach performed best in bothdetecting heterogeneity and in returning more accurate overall effect estimates. Re-analysing all meta-analyses with thisnew method we found that in cases where heterogeneity had originally been detected but ignored, 17–20% of thestatistical conclusions changed. Rates were much lower where the original analysis did not detect heterogeneity or took itinto account, between 1% and 3%.

Conclusions: When evidence for heterogeneity is lacking, standard practice is to assume homogeneity and apply a simplerfixed-effect meta-analysis. We find that assuming homogeneity often results in a misleading analysis, since heterogeneity isvery likely present but undetected. Our new method represents a small improvement but the problem largely remains,especially for very small meta-analyses. One solution is to test the sensitivity of the meta-analysis conclusions to assumedmoderate and large degrees of heterogeneity. Equally, whenever heterogeneity is detected, it should not be ignored.

Citation: Kontopantelis E, Springate DA, Reeves D (2013) A Re-Analysis of the Cochrane Library Data: The Dangers of Unobserved Heterogeneity in Meta-Analyses. PLoS ONE 8(7): e69930. doi:10.1371/journal.pone.0069930

Editor: Tim Friede, University Medical Center Gottingen, Germany

Received February 20, 2013; Accepted June 13, 2013; Published July 26, 2013

Copyright: � 2013 Kontopantelis et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: EK was partly supported by a National Institute for Health Research (NIHR) School for Primary Care Research fellowship in primary health care. Thefunders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received forthis study.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: e.kontopantelis@manchester.ac.uk

Introduction

Meta-analysis (MA), the methodologies of synthesising existing

evidence to answer a clinical or other research question, is a

relatively young and dynamic area of research. The furore of

methodological activity reflects the clinical importance of meta-

analysis and its potential to provide conclusive answers, rather

than incremental knowledge contributions, much more cheaply

than a new large Randomised Clinical Trial (RCT).

The best analysis approach is an Individual Patient Data (IPD)

meta-analysis, which requires access to patient level data and

considerably more effort (to obtain the datasets mainly). However,

with IPD data, clinical and methodological heterogeneity,

arguably the biggest concern for meta-analysts, can be addressed

through patient-level covariate controlling or subgroup analyses

when covariate data are not available across all studies.

When the original data are unavailable, researchers have to

combine the evidence in a two stage process, retrieving the

relevant summary effects statistics from publications and using a

suitable meta-analysis model to calculate an overall effect estimate

mm. Model selection depends on the estimated heterogeneity, or

between-study variance, and its presence usually leads to the

adoption of a random-effects (RE) model. The alternative, the

fixed-effects model (FE), is used when meta-analysts, for theoretical

or practical reasons, decide not to adjust for heterogeneity, or have

assumed or estimated the between-study variability to be zero.

Different approaches exist for combining individual study results

into an overall estimate of effect under the fixed- or random-effects

assumptions: inverse variance, Mantel-Haenszel and Peto [1].

Inverse variance approaches are the most flexible and are

suitable for continuous or dichotomous data through a fixed-effect

or one of numerous random-effects methods. The DerSimonian

and Laird [2] method (DL), a moment-based estimator, is the

PLOS ONE | www.plosone.org 1 July 2013 | Volume 8 | Issue 7 | e69930

The Stata Journal (2010)10, Number 3, pp. 395–407

metaan: Random-effects meta-analysis

Evangelos KontopantelisNational Primary Care

Research & Development CentreUniversity of Manchester

Manchester, UK

e.kontopantelis@manchester.ac.uk

David ReevesHealth Sciences Primary Care

Research GroupUniversity of Manchester

Manchester, UK

david.reeves@manchester.ac.uk

Abstract. This article describes the new meta-analysis command metaan, whichcan be used to perform fixed- or random-effects meta-analysis. Besides the stan-dard DerSimonian and Laird approach, metaan offers a wide choice of availablemodels: maximum likelihood, profile likelihood, restricted maximum likelihood,and a permutation model. The command reports a variety of heterogeneity mea-sures, including Cochran’s Q, I2, H2

M , and the between-studies variance estimatebτ2. A forest plot and a graph of the maximum likelihood function can also begenerated.

Keywords: st0201, metaan, meta-analysis, random effect, effect size, maximumlikelihood, profile likelihood, restricted maximum likelihood, REML, permutationmodel, forest plot

1 Introduction

Meta-analysis is a statistical methodology that integrates the results of several inde-pendent clinical trials in general that are considered by the analyst to be “combinable”(Huque 1988). Usually, this is a two-stage process: in the first stage, the appropriatesummary statistic for each study is estimated; then in the second stage, these statis-tics are combined into a weighted average. Individual patient data (IPD) methodsexist for combining and meta-analyzing data across studies at the individual patientlevel. An IPD analysis provides advantages such as standardization (of marker values,outcome definitions, etc.), follow-up information updating, detailed data-checking, sub-group analyses, and the ability to include participant-level covariates (Stewart 1995;Lambert et al. 2002). However, individual observations are rarely available; addition-ally, if the main interest is in mean effects, then the two-stage and the IPD approachescan provide equivalent results (Olkin and Sampson 1998).

This article concerns itself with the second stage of the two-stage approach to meta-analysis. At this stage, researchers can select between two main approaches—the fixed-effects (FE) or the random-effects model—in their efforts to combine the study-levelsummary estimates and calculate an overall average effect. The FE model is simplerand assumes the true effect to be the same (homogeneous) across studies. However, ho-mogeneity has been found to be the exception rather than the rule, and some degree oftrue effect variability between studies is to be expected (Thompson and Pocock 1991).Two sorts of between-studies heterogeneity exist: clinical heterogeneity stems from dif-

c© 2010 StataCorp LP st0201

JSS Journal of Statistical SoftwareMMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/

Simulation-based power calculations for mixed

effects modelling: ipdpower in Stata

Evangelos KontopantelisNIHR School for Primary Care Research

University of Manchester

David A SpringateInstitute of Population Health

University of Manchester

Rosa ParisiManchester Pharmacy School

University of Manchester

David ReevesInstitute of Population Health

University of Manchester

Abstract

Simulations are a practical and reliable approach to power calculations, especially formulti-level mixed effects models where the analytic solutions can be very complex. Inaddition, power calculations are model-specific and multi-level mixed effects models aredefined by a plethora of parameters. In other words, model variations in this context arenumerous and so are the tailored algebraic calculations. This article describes ipdpower inStata, a new simulations-based command that calculates power for mixed effects two-leveldata structures. Although the command was developed having individual patient datameta-analyses and primary care databases analyses in mind, where patients are nestedwithin studies and general practices respectively, the methods apply to any two-levelstructure.

Keywords: Stata, ipdpower, power, coverage, meta analysis, multi level, mixed effects, randomeffects, individual patient data, IPD, primary care databases, PCD.

1. Introduction

The size of primary care databases (PCDs) allows for investigations that cannot normallybe undertaken in much smaller randomised controlled trials (RCTs), such as the moderatingeffect of a patient characteristic on the effect of an intervention. However, researchers quiteoften underestimate the numbers needed to detect such effects and assume that the size ofthe database alone guarantees adequate power for any type of investigation. The essential

Article

Performance of statisticalmethods for meta-analysiswhen true study effects arenon-normally distributed: Asimulation study

Evangelos Kontopantelis1 and David Reeves2

Abstract

Meta-analysis (MA) is a statistical methodology that combines the results of several independent studies

considered by the analyst to be ‘combinable’. The simplest approach, the fixed-effects (FE) model, assumes

the true effect to be the same in all studies, while the random-effects (RE) family of models allows the true

effect to vary across studies. However, all methods are only correct asymptotically, while some RE models

assume that the true effects are normally distributed. In practice, MA methods are frequently applied

when study numbers are small and the normality of the effect distribution unknown or unlikely. In this

article, we discuss the performance of the FE approach and seven frequentist RE MA methods:

DerSimonian–Laird, Q-based, maximum likelihood, profile likelihood, Biggerstaff–Tweedie, Sidik–

Jonkman and Follmann–Proschan. We covered numerous scenarios by varying the MA sizes (small to

moderate), the degree of heterogeneity (zero to very large) and the distribution of the effect sizes

(normal, skew-normal and ‘extremely’ non-normal). Performance was evaluated in terms of coverage

(Type I error), power (Type II error) and overall effect estimation (accuracy of point estimates and error

intervals).

Keywords

meta-analysis, non-normal, profile likelihood, power, coverage, simulation, DerSimonian–Laird,

Biggestaff–Tweedie, Sidik–Jonkman, permutations

1National Primary Care Research and Development Centre, University of Manchester, Williamson Building, 5th Floor, Oxford Road,

M13 9PL, UK2Health Sciences Primary Care Research Group, University of Manchester, Williamson Building, 5th Floor, Oxford Road, M13 9PL,

UK

Corresponding author:

Evangelos Kontopantelis, National Primary Care Research and Development Centre, University of Manchester, Williamson Building,

5th Floor, Oxford Road, M13 9PL, UK

Email: e.kontopantelis@manchester.ac.uk

Statistical Methods in Medical Research

21(4) 409–426

! The Author(s) 2010

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DOI: 10.1177/0962280210392008

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Kontopantelis Software and model selection challenges in meta-analysis