(20181118, ada) distributed analysis in multi-center studies...methods that allow robust and...

Distributed analysis in multi-center studies

Sharing of individual-level data across health plans or healthcare delivery systems continues to be challenging due to concerns about loss of patient privacy, unauthorized uses of transferred data, inaccurate analysis or interpretation of data, or contractual or legal restrictions. Although these challenges can be addressed in part by proper governance and appropriate updates to existing regulations, newer privacy-protecting analytic and data-sharing methods offer another potential solution. This presentation will describe the use of privacy-protecting analytic methods that allow robust and flexible statistical analysis using aggregate-level information, without centralized pooling of individual-level datasets across data sources. We will present several comparative safety and effectiveness studies of medical treatments that employ these methods to generate actionable real-world evidence.

1. Toh S, Gagne JJ, Rassen JA, Fireman BH, Kulldorff M, Brown JS. Confounding adjustment incomparative effectiveness research conducted within distributed research networks. MedCare 2013:51(8 Suppl 3):S4-S10

2. Toh S, Hampp C, Reichman ME, Graham DJ, Balakrishnan S, Pucino F, Hamilton J, Lendle S,Iyer A, Rucker M, Pimentel M, Nathwani N, Griffin MR, Brown NJ, Fireman BH. Risk ofhospitalized heart failure among new users of saxagliptin, sitagliptin, and otherantihyperglycemic drugs: A retrospective cohort study. Ann Intern Med 2016;164(11):705-714 (PMC5178978)

3. Toh S, Reichman ME, Graham DJ, Hampp C, Zhang R, Butler MG, Iyer A, Rucker M,Pimentel M, Hamilton J, Lendle S, Fireman BH; for the Mini-Sentinel AMI-SaxagliptinSurveillance Writing Group. Prospective post-marketing surveillance of acute myocardialinfarction in new users of saxagliptin: A population-based study. Diabetes Care2018;41(1):39-48

Distributed analysis in multi‐center studies

Darren Toh, ScDDepartment of Population Medicine

Harvard Medical School & Harvard Pilgrim Health Care InstituteBoston, MA

November 18, 2018

Disclosures

Research support• Patient‐Centered Outcomes Research Institute (ME‐1403‐11305)• Office of the Assistant Secretary for Planning and Evaluation & Food and Drug Administration

(HHSF223200910006I)• National Institutes of Health (U01EB023683)• Agency for Healthcare Research and Quality (R01HS026214)

Board of Directors, International Society for Pharmacoepidemiology

My spouse is an employee of Biogen

2

Overview

Evolution of multi‐center studies

Analytic methods in multi‐center studies

Select examples

Discussion

3

Overview



Select examples

Discussion

4

Multi‐center studies

Many studies are now done in multi‐center settings

5

Why do multi‐center studies?

6

Larger sample sizes• Allow studies of rare treatments or rare outcomes• Allow studies in specific subpopulations• Allow studies to be done more quickly

More diverse populations• Allow more generalizable findings• Allow assessment of treatment effect heterogeneity

Multi‐center studies v1.0

Analysis center

7


8

Pooling study‐specific individual‐level datasets

Typical datasets shared in multi‐center studies v1.0

PatID Exposure Outcome Time Age Sex DM HTN CVD …

001 1 0 312 33 1 0 1 1 …

002 0 0 40 45 1 0 1 0 …

003 0 0 365 76 0 0 0 0 …

004 0 0 200 56 0 1 0 0 …

005 0 1 2 21 0 0 1 0 …

006 1 1 15 80 1 0 0 1 …

007 1 0 4 65 1 1 0 1 …

008 1 0 145 77 0 1 0 0 …

009 0 1 33 48 1 0 0 0 …

010 0 0 98 52 1 0 0 0 …

011 0 0 34 32 0 0 0 0 …

… … … … … … … … … …

9



001 1 0 312 33 1 0 1 1 …

002 0 0 40 45 1 0 1 0 …

003 0 0 365 76 0 0 0 0 …

004 0 0 200 56 0 1 0 0 …

005 0 1 2 21 0 0 1 0 …

006 1 1 15 80 1 0 0 1 …

007 1 0 4 65 1 1 0 1 …

008 1 0 145 77 0 1 0 0 …

009 0 1 33 48 1 0 0 0 …

010 0 0 98 52 1 0 0 0 …

011 0 0 34 32 0 0 0 0 …

… … … … … … … … … …

10

Each row represents an individual



001 1 0 312 33 1 0 1 1 …

002 0 0 40 45 1 0 1 0 …

003 0 0 365 76 0 0 0 0 …

004 0 0 200 56 0 1 0 0 …

005 0 1 2 21 0 0 1 0 …

006 1 1 15 80 1 0 0 1 …

007 1 0 4 65 1 1 0 1 …

008 1 0 145 77 0 1 0 0 …

009 0 1 33 48 1 0 0 0 …

010 0 0 98 52 1 0 0 0 …

011 0 0 34 32 0 0 0 0 …

… … … … … … … … … …

11

Each column represents a covariate


12


001 1 0 312 33 1 0 1 1 …

002 1 0 40 45 1 0 1 0 …

003 1 0 365 76 0 0 0 0 …

004 1 0 200 56 0 1 0 0 …

005 0 1 2 21 0 0 1 0 …

006 0 1 15 80 1 0 0 1 …

007 0 0 4 65 1 1 0 1 …

008 0 0 145 77 0 1 0 0 …

… … … … … … … … … …


001 0 1 35 44 0 1 3 0 …

002 0 1 213 54 0 1 1 1 …

003 0 1 453 78 0 0 4 1 …

004 0 0 58 87 1 0 3 1 …

005 1 0 31 22 1 0 3 0 …

006 1 0 56 46 0 1 2 0 …

007 1 0 123 53 0 1 1 1 …

008 1 0 546 35 0 0 3 0 …

… … … … … … … … … …

Site PatID Exposure Outcome Time Age Sex DM HTN CVD …

1 001 1 0 312 33 1 0 1 1 …

1 002 1 0 40 45 1 0 1 0 …

1 003 1 0 365 76 0 0 0 0 …

1 004 1 0 200 56 0 1 0 0 …

1 005 0 1 2 21 0 0 1 0 …

1 006 0 1 15 80 1 0 0 1 …

1 007 0 0 4 65 1 1 0 1 …

1 008 0 0 145 77 0 1 0 0 …

… … … … … … … … … … …

2 001 0 1 35 44 0 1 3 0 …

2 002 0 1 213 54 0 1 1 1 …

2 003 0 1 453 78 0 0 4 1 …

2 004 0 0 58 87 1 0 3 1 …

2 005 1 0 31 22 1 0 3 0 …

2 006 1 0 56 46 0 1 2 0 …

2 007 1 0 123 53 0 1 1 1 …

2 008 1 0 546 35 0 0 3 0 …

… … … … … … … … … … …

Data Partner 1

Data Partner 2


Individual data partners

Site 1 Site 2

Site 3 Site 4

Data standardization(common data model)

Site 1

Site 2

Site 3

Site 4

Data accessible to research projects

• Research projects

• Programs written against common data model

Data quality improvement feedback loop

Adapted from: http://www.hcsrn.org/asset/b9efb268‐eb86‐400e‐8c74‐2d42ac57fa4F/VDW.Infographic031511.jpg 13

Data standardization – Common data model

14

Review & Run Query

Review & Return Results

Data Partner 1

EnrollmentDemographicsUtilizationPharmacy

Etc

1. User creates and submits query

Review & Run Query


Data Partner 2


Etc

Analysis Center

Secure Network Portal

1

Distributed analysis in networks with common data model

15

Review & Run Query


Data Partner 1


Etc


2. Data partners retrieve query

2

Review & Run Query


Data Partner 2


Etc

Analysis Center


1


16

Review & Run Query


Data Partner 1


Etc



3. Data partners review and run query against their local data

4. Data partners review results

2 3 4

Review & Run Query


Data Partner 2


Etc

3 4

Analysis Center


1


17

Review & Run Query


Data Partner 1


Etc



3. Data partners review and run query against their local data

4. Data partners review results

5. Data partners return results via secure network

6. Results are aggregated and reported

2 3 45

6

Review & Run Query


Data Partner 2


Etc

3 4

Analysis Center


1


18


19


001 1 0 312 33 1 0 1 1 …

002 1 0 40 45 1 0 1 0 …

003 1 0 365 76 0 0 0 0 …

004 1 0 200 56 0 1 0 0 …

005 0 1 2 21 0 0 1 0 …

006 0 1 15 80 1 0 0 1 …

007 0 0 4 65 1 1 0 1 …

008 0 0 145 77 0 1 0 0 …

… … … … … … … … … …


001 0 1 35 44 0 1 3 0 …

002 0 1 213 54 0 1 1 1 …

003 0 1 453 78 0 0 4 1 …

004 0 0 58 87 1 0 3 1 …

005 1 0 31 22 1 0 3 0 …

006 1 0 56 46 0 1 2 0 …

007 1 0 123 53 0 1 1 1 …

008 1 0 546 35 0 0 3 0 …

… … … … … … … … … …

Site PatID Exposure Outcome Time Age Sex DM HTN CVD …

1 001 1 0 312 33 1 0 1 1 …

1 002 1 0 40 45 1 0 1 0 …

1 003 1 0 365 76 0 0 0 0 …

1 004 1 0 200 56 0 1 0 0 …

1 005 0 1 2 21 0 0 1 0 …

1 006 0 1 15 80 1 0 0 1 …

1 007 0 0 4 65 1 1 0 1 …

1 008 0 0 145 77 0 1 0 0 …

… … … … … … … … … … …

2 001 0 1 35 44 0 1 3 0 …

2 002 0 1 213 54 0 1 1 1 …

2 003 0 1 453 78 0 0 4 1 …

2 004 0 0 58 87 1 0 3 1 …

2 005 1 0 31 22 1 0 3 0 …

2 006 1 0 56 46 0 1 2 0 …

2 007 1 0 123 53 0 1 1 1 …

2 008 1 0 546 35 0 0 3 0 …

… … … … … … … … … … …

Data Partner 1

Data Partner 2

Concerns about data sharing in multi‐center studies v1 & v2

Loss of patient privacy

Unauthorized uses of data

Inaccurate analysis or interpretation of data

Disclosures of sensitive institutional or corporate information

Contractual restrictions

20

Data sharing – A balancing act

Granularity or identifiability of

information

Analytic flexibility

21


Analysis Center

22


Pooling study‐specific summary‐level datasets23

Overview



Select examples

Discussion

24

Privacy‐protecting methods for multi‐center studies v3.0

Summary score‐based methods

Meta‐analysis of database‐specific effect estimates

Distributed regression

25

Summary scores

PS: Propensity scores DRS: Disease risk scores

26

Treatment Outcome

Confounders

DRSPS

Individual‐level dataset with individual covariates


001 1 0 312 33 1 0 1 1 …

002 0 0 40 45 1 0 1 0 …

003 0 0 365 76 0 0 0 0 …

004 0 0 200 56 0 1 0 0 …

005 0 1 2 21 0 0 1 0 …

006 1 1 15 80 1 0 0 1 …

007 1 0 4 65 1 1 0 1 …

008 1 0 145 77 0 1 0 0 …

009 0 1 33 48 1 0 0 0 …

010 0 0 98 52 1 0 0 0 …

011 0 0 34 32 0 0 0 0 …

… … … … … … … … … …

27

Individual‐level dataset with summary scores

PatID Exposure Outcome Time PS

001 1 0 312 0.33

002 0 0 40 0.21

003 0 0 365 0.56

004 0 0 200 0.11

005 0 1 2 0.97

006 1 1 15 0.56

007 1 0 4 0.40

008 1 0 145 0.22

009 0 1 33 0.43

010 0 0 98 0.78

011 0 0 34 0.38

… … … … …

28

Summary score‐based method #1 – Matching

29

Persons in exposed

Persons in unexposed

Events in exposed

Events in unexposed

500 500 80 75


001 1 0 312 0.33

002 0 0 40 0.21

003 0 0 365 0.56

004 0 0 200 0.11

005 0 1 2 0.97

006 1 1 15 0.56

007 1 0 4 0.40

008 1 0 145 0.22

009 0 1 33 0.43

010 0 0 98 0.78

011 0 0 34 0.38

… … … … …

Summary score‐based method #1 – Matching

30

Persons in exposed


Events in exposed

Events in unexposed

500 500 87 85

Persons in exposed


Events in exposed

Events in unexposed

400 400 68 65

Site Persons in exposed


Events in exposed

Events in unexposed

1 500 500 87 85

2 400 400 68 65

Data Partner 1

Data Partner 2

Summary score‐based method #2 – Stratification

31


001 1 0 312 0.33

002 0 0 40 0.21

003 0 0 365 0.56

004 0 0 200 0.11

005 0 1 2 0.97

006 1 1 15 0.56

007 1 0 4 0.40

008 1 0 145 0.22

009 0 1 33 0.43

010 0 0 98 0.78

011 0 0 34 0.38

… … … … …

PS or DRS stratum

Persons in exposed


Events in exposed

Events in unexposed

1 200 150 30 352 150 100 20 403 200 180 21 214 150 200 26 18

Summary score‐based method #3 – Risk set analysis

32


001 1 0 312 0.33

002 0 0 40 0.21

003 0 0 365 0.56

004 0 0 200 0.11

005 0 1 2 0.97

006 1 1 15 0.56

007 1 0 4 0.40

008 1 0 145 0.22

009 0 1 33 0.43

010 0 0 98 0.78

011 0 0 34 0.38

… … … … …

Event Event time Event exposed

Risk set exposed

Risk set unexposed

1 8 0 300 2992 12 1 296 2953 20 1 290 2884 21 0 286 283… … … … …

Meta‐analysis of database‐specific effect estimates

33


001 1 0 312 33 1 0 1 1 …

002 0 0 40 45 1 0 1 0 …

003 0 0 365 76 0 0 0 0 …

004 0 0 200 56 0 1 0 0 …

005 0 1 2 21 0 0 1 0 …

006 1 1 15 80 1 0 0 1 …

007 1 0 4 65 1 1 0 1 …

008 1 0 145 77 0 1 0 0 …

009 0 1 33 48 1 0 0 0 …

010 0 0 98 52 1 0 0 0 …

011 0 0 34 32 0 0 0 0 …

… … … … … … … … … …

Hazard ratio Lower 95% CI Upper 95% CI

2.97 1.95 4.52


34

Analyst inputs individual‐level dataset into statistical software

Statistical software produces final results

Statistical software produces intermediate statistics as part of

computing process

ID E X1 X2 Y

A001 0 13.89 3.42 28.70

A002 1 18.10 1.29 27.90

A003 0 6.41 4.86 33.10

A004 1 16.30 1.45 17.20

A005 1 17.57 2.51 21.70

… … … … …

A100 0 5.78 2.53 23.76

Type Name Intercept E X1 X2 Y

SSCP Intercept 100.0 52.0 1157.1 405.9 2235.5

SSCP E 52.0 52.0 813.2 138.1 1060.9

SSCP X1 1157.1 813.2 17751.3 3458.7 23815.8

SSCP X2 405.9 138.1 3458.7 2240.8 9572.3

SSCP Y 2235.5 1060.9 23815.8 9572.3 56911.9

MEAN 1.0 0.5 11.6 4.1 22.4

STD 0.0 0.5 6.6 2.5 8.4

N 100 100 100 100 100

VariableParameter estimate

Standard error

Intercept 25.4540 3.7959

E ‐0.4323 1.7865

X1 ‐0.5643 0.1432

X2 ‐0.6564 0.4532


35




computing process

ID E X1 X2 Y

A001 0 13.89 3.42 28.70

A002 1 18.10 1.29 27.90

A003 0 6.41 4.86 33.10

A004 1 16.30 1.45 17.20

A005 1 17.57 2.51 21.70

… … … … …

A100 0 5.78 2.53 23.76


SSCP Intercept 100.0 52.0 1157.1 405.9 2235.5

SSCP E 52.0 52.0 813.2 138.1 1060.9

SSCP X1 1157.1 813.2 17751.3 3458.7 23815.8

SSCP X2 405.9 138.1 3458.7 2240.8 9572.3

SSCP Y 2235.5 1060.9 23815.8 9572.3 56911.9

MEAN 1.0 0.5 11.6 4.1 22.4

STD 0.0 0.5 6.6 2.5 8.4

N 100 100 100 100 100


Standard error

Intercept 25.4540 3.7959

E ‐0.4323 1.7865

X1 ‐0.5643 0.1432

X2 ‐0.6564 0.4532

“Regular” regression shares this


36




computing process

ID E X1 X2 Y

A001 0 13.89 3.42 28.70

A002 1 18.10 1.29 27.90

A003 0 6.41 4.86 33.10

A004 1 16.30 1.45 17.20

A005 1 17.57 2.51 21.70

… … … … …

A100 0 5.78 2.53 23.76


SSCP Intercept 100.0 52.0 1157.1 405.9 2235.5

SSCP E 52.0 52.0 813.2 138.1 1060.9

SSCP X1 1157.1 813.2 17751.3 3458.7 23815.8

SSCP X2 405.9 138.1 3458.7 2240.8 9572.3

SSCP Y 2235.5 1060.9 23815.8 9572.3 56911.9

MEAN 1.0 0.5 11.6 4.1 22.4

STD 0.0 0.5 6.6 2.5 8.4

N 100 100 100 100 100


Standard error

Intercept 25.4540 3.7959

E ‐0.4323 1.7865

X1 ‐0.5643 0.1432

X2 ‐0.6564 0.4532

Distributed regression shares this

Overview



Select examples

Discussion

37

Example 1

http://www.hopkinsmedicine.org/healthlibrary/test_procedures/gastroenterology/laparoscopic_adjustable_gastric_banding_135,63/

http://www.hopkinsmedicine.org/healthlibrary/test_procedures/gastroenterology/roux‐en‐y_gastric_bypass_weight‐loss_surgery_135,65/

38

Study design

•≥21 years at time of bariatric surgery•≥1 BMI of 35kg/m2 or greater •Continuous enrollment w/ benefits•No prior bariatric surgery•No prior diagnosis of study outcome

1/1/2005

Time

Contributing person‐times

12/31/2010Start of follow up (discharge date)

•Re‐hospitalization•Death•Health plan disenrollment•12/31/2010•730 days of follow‐up

365 days

Index bariatric hospitalization

39Toh et al, Med Care, 2014;52:664‐668

Confounders

40

Age Asthma*Sex Deep vein thrombosis*Race/ethnicity Pulmonary embolism*Diabetes* Congestive heart failure*Baseline BMI* Hyperlipidemia*Year of procedure Coronary artery disease*Charlson comorbidity score* Oxygen use*Atrial fibrillation* Assistive walking device*

GERD* Smoking status*Hypertension* Blood pressure*Sleep Apnea* Length of stay assoc. with procedure

*Identified during the 365‐day baseline period prior to the index bariatric hospitalization

Toh et al, Med Care, 2014;52:664‐668

Statistical analysis

Propensity score stratification

Analysis• Pooled patient‐level data analysis (benchmark)• Risk set‐based analysis• PS‐stratified analysis (by quintile)• Meta‐analysis of site‐specific effect estimates

41Toh et al, Med Care, 2014;52:664‐668

Select baseline patient characteristicsCharacteristics Adjustable gastric band (n=1,550) Roux‐en‐y gastric bypass (n=5,792)

N %* N %*

Mean age (SD) 46.7 11.2 45.7 10.7

Age > 65 years 76 4.9 141 2.4

Female sex 1,266 81.7 4,823 83.3

Race/ethnicityBlack or African American 137 8.8 522 9.0

White 1,130 72.9 3,840 66.3

Hispanic 142 9.2 769 13.3

Other 62 4.0 280 4.8

Unknown 79 5.1 381 6.6

Baseline BMI

30‐34.9 96 6.2 174 3.0

35‐39.9 480 31.0 1,410 24.3

40‐49.9 813 52.4 3,126 54.0

≥50 161 10.4 1,082 18.7

42Toh et al, Med Care, 2014;52:664‐668

Individual‐level data analysis, by site

Site Adjusted HR 95% CISite 1 0.68 0.45, 1.02Site 2 0.65 0.37, 1.15Site 3 0.52 0.26, 1.04Site 4 0.72 0.35, 1.50Site 5 0.82 0.46, 1.48Site 6 0.32 0.13, 0.75Site 7 0.79 0.62, 1.01

43Toh et al, Med Care, 2014;52:664‐668

Results, by method

Method AdjustedHR 95% CI

Individual‐level 0.71 0.59, 0.84

Risk set 0.71 0.59, 0.84

PS stratification 0.70 0.59, 0.83

Meta‐analysis 0.71 0.60, 0.84

44Toh et al, Med Care, 2014;52:664‐668

Example 2 – Distributed regression

45

Distributed Regression vs. Pooled Patient‐Level Regression – LINEAR

CovariatesDistributed Regression Pooled Patient‐Level Differences in

Parameter EstimatesDifferences in Standard ErrorsParameter Estimates Standard Errors Parameter Estimates Standard Errors

Intercept 35.50548 1.57690 35.50548 1.57690 ‐8.38E‐13 2.26E‐14Variable 1 ‐0.27283 0.04401 ‐0.27283 0.04401 4.44E‐16 9.92E‐16Variable 2 ‐1.01582 0.23259 ‐1.01582 0.23259 1.09E‐13 3.22E‐15Variable 3 ‐0.73017 0.07229 ‐0.73017 0.07229 3.54E‐14 1.32E‐15

Distributed Regression vs. Pooled Patient‐Level Regression – LOGISTIC



Intercept 2.49660 0.49057 2.49660 0.49060 1.33E‐15 9.99E‐16Variable 1 ‐0.14465 0.03686 ‐0.14460 0.03690 2.04E‐13 ‐2.97E‐14Variable 2 ‐0.14105 0.06976 ‐0.14100 0.06980 1.38E‐14 ‐2.22E‐16Variable 3 ‐0.13889 0.02376 ‐0.13890 0.02380 ‐2.42E‐14 ‐2.19E‐16

Distributed Regression vs. Pooled Patient‐Level Regression – COX



Variable 1 ‐0.06692 0.02084 ‐0.06692 0.02084 ‐1.39E‐16 2.78E‐17Variable 2 ‐0.34644 0.19024 ‐0.34644 0.19024 2.22E‐16 ‐2.78E‐17Variable 3 0.09653 0.02724 0.09653 0.02724 ‐1.80E‐16 1.73E‐17

46

Example 3 – PCORnet Bariatric Study

Use of bariatric surgery has expanded considerably

Evidence on the comparative effectiveness and safety of these procedures is limited

Study design

47

Main analysis Aggregate analysisComparisons • RYGB vs. SG

• RYGB vs. AGB• AGB vs. SG

• RYGB vs. SG

Outcomes • Weight change 1, 3, and 5 yrs post‐surgery

• Diabetes remission and relapse• Major adverse events

• Weight change 1 yr post surgery

Analysis • One model that combines all data• Additional data‐driven approaches

to select covariates

• Site‐specific PS model• Fixed set of covariates

Combining propensity scores with distributed regression

50

VariableParameter estimate Standard error

Pooled individual‐level data analysis


RYGB vs. SG ‐0.05470 0.00113

PS stratum 1 Reference Reference

PS stratum 2 ‐0.00754 0.00209

PS stratum 3 ‐0.00671 0.00210

PS stratum 4 ‐0.00717 0.00211

PS stratum 5 0.00034218 0.00212

PS stratum 6 ‐0.00583 0.00213

PS stratum 7 ‐0.00135 0.00214

PS stratum 8 ‐0.00435 0.00216

PS stratum 9 ‐0.00523 0.00218

PS stratum 10 ‐0.00812 0.00222

Combining propensity scores with distributed regression

51

VariableParameter estimate Standard error





RYGB vs. SG ‐0.05470 ‐0.05470 0.00113 0.00113

PS stratum 1 Reference Reference Reference Reference

PS stratum 2 ‐0.00754 ‐0.00754 0.00209 0.00209

PS stratum 3 ‐0.00671 ‐0.00671 0.00210 0.00210

PS stratum 4 ‐0.00717 ‐0.00717 0.00211 0.00211

PS stratum 5 0.00034218 0.00034218 0.00212 0.00212

PS stratum 6 ‐0.00583 ‐0.00583 0.00213 0.00213

PS stratum 7 ‐0.00135 ‐0.00135 0.00214 0.00214

PS stratum 8 ‐0.00435 ‐0.00435 0.00216 0.00216

PS stratum 9 ‐0.00523 ‐0.00523 0.00218 0.00218

PS stratum 10 ‐0.00812 ‐0.00812 0.00222 0.00222

52www.sentinelinitiative.org/sites/default/files/Drugs/Assessments/Mini‐Sentinel_AMI‐and‐Anti‐Diabetic‐Agents_Protocol_0.pdf

Example 4: Prospective surveillance of saxagliptin

52

53http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm071627.pdf 53

54

SAVOR‐TIMI 53 Trial

54

55www.sentinelinitiative.org/sites/default/files/Drugs/Assessments/Mini‐Sentinel_AMI‐and‐Anti‐Diabetic‐Agents_Protocol_0.pdf

Prospective surveillance of saxagliptin

55

56

Saxagliptin vs. sitagliptin

56

57

Saxagliptin vs. pioglitazone

57

58

Saxagliptin vs. sulfonylureas

58

59

Saxagliptin vs. long‐acting insulin

59

60

Comparisons with SAVOR‐TIMI 53 trial

Characteristics SAVOR‐TIMI 53 Trial Mini‐Sentinel surveillance*

Comparator Placebo Select anti‐hyperglycemics

No. saxagliptin users 8,280 82,264

No. comparator users 8,212 146,045 to 452,969

No. AMI in saxagliptin 265 94 to 171

No. AMI in comparator 278 75 to 1,085

Length of follow‐up 2.1 years (median) 4 to 8 months (mean)

Statistical analysis Intention‐to‐treat As‐treated

Hazard ratio for AMI 0.95 (95% CI: 0.80, 1.12) 0.54 to 1.17

* From end‐of‐surveillance analysis that included all patients

Interim results from the first 5 sequential analyses were made available to FDA prior to the publication of SAVOR‐

TIMI 53 findings

60

Overview



Select examples

Discussion

61

Analytical flexibility vs. granularity of information

62

Analytic flexibility

Individual‐level data

with individual covariates

Effect‐estimate data


with summary scores

Summary‐table data

Risk‐set data

Intermediate statistics

Privacy protection


Covariate summarization technique

Individual covariates*

Propensity scores

Disease risk scores

Summary scores + individual covariates

A hybrid of above

Data sharing approach



Risk‐set data



Covariate adjustment technique

Matching

Stratification

Restriction

Weighting

Modeling

Outcome type

Continuous

Binary

Count

Survival

63

What to share? How to share? What can we do? What outcome?


Covariate summarization technique

Individual covariates

Propensity scores

Disease risk scores

Summary scores + individual covariatesA hybrid of above

Data sharing approach



Risk‐set data



Covariate adjustment technique

Matching

Stratification

Restriction

Weighting

Modeling

Outcome type

Continuous

Binary

Count

Survival

64

Conclusion

A suite of analytic methods are available for multi‐center studies

There are often trade‐offs between analytic flexibility and identifiability of information shared

Some newer methods offer excellent analytic flexibility and good privacy protection

65

[email protected]

@darrentoh_epi

https://www.distributedanalysis.org

66

(20181118, ada) distributed analysis in multi-center studies...methods that allow robust and...

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