applying meta-analysis to trauma registry ammarin thakkinstian, ph.d. clinical epidemiology unit...

Applying meta-analysis to trauma registry

Ammarin Thakkinstian, Ph.D. Clinical Epidemiology Unit

Faculty of Medicine, Ramathibodi Hospital

Tel: 2011269,2011762 Fax: 02-2011284

e-mail: raatk@mahidol.ac.th

Meta-analysis

• A tool for pooling results/data of the same topics from different sources/centres in order to – estimates treatment/intervention effects– leading to reduces probability of false

negative results– potentially to a more timely introduction of

effective treatments/intervention/program– Objective evidence & quantitative conclusion

Type of meta-analysis

• Summary data– Unit of analysis is study– Mean (SD) – Count/frequency data by intervention &

outcome– Person-time data

Summary-data

• Continuous data

Studyi N MeanSD

Rx/Exp+ N1 Mean1 SD1

Cont/Exp- N2 Mean2 SD2

Summary-data

• Categorical data

Studyi Case Control

Rx/Exp+Cont/Exp-

ac

bd

Type of meta-data

• Individual patient data (IPD)– Raw databases – Unit of analysis is patient– Analogous to multi-centre trials – More retrospective than prospective – Data registry

IPD

– Carry out data checking (data validation)– Better standardization of information

•Categorization of eligible participants•Definition of Outcomes•Variables’ Classification

– ICD-10– Type of trauma – AIS

IPD– Flexible to apply statistic modeling – Better adjust for confounders & adjust for the

same confounders simultaneously– More flexible to assess interaction effects – More flexible and capable in assessing cause

of heterogeneity– Allow to assess which subgroup of patients

(centre) that intervention/program may/may not work

– Establishment of international networks of collaborating investigators

IPD• Disadvantage

– Data quality •Missing data•Data validation

– More cost & time consuming– Substantial effort and infrastructure require to

•Develop & administer a standardized protocol

•Collect, manage, & data management •Communicate with collaborators

Data collection & management– Data Registry

Databases

Data coding

Data entry

Cleaning Checking

Validate data

QC

Hospitals

Data manager

Validated Data

Retrieve databases

Combine data

Re-check data

Analyse data

Statistician

Report results

Writing report (manuscript)

Publish (annual, twice/year)

Data analysis

• Heterogeneity test – Different source data are homogeneous?

• Homogeneity

n/programinterentiojth theofeffect thet

centreith theofeffect the

j

i

ijkijijk

c

ctY

Analysis • Heterogeneity

hospital j in individual ifor error individuale

effect) ion(intervent slope regressionβ

hospital jfor intercept

j hospital in i individual

ththij

1

0j

β

ij

exββY ijijojij 1

Outcomes

• Death/alive • Disability/Non-disability • Complications

– Infection– Fracture

• Hospitalization • Hospital days• QoL• Cost

Count (discrete) outcome • Poisson regression

– Number of death – Number of infection – Number of disability – Number of fracture

Intervention Period Pop No. of death

Death /1000

RR

Audited 2001-2005 87870 3926 45 0.65 (0.62, 0.67)

Non-audited 1999-2003 228243 13120 57 1

RRe

ex

jj

j

j

ij

ijijjij

11

1

101

0

11

1

hospital-audited if 1hospital audited-non if 0i

1

lnln

:hospital audited-non versus-Audited

ln

:hospital-Audited-Non

ln

:hospital-Audited

j hospitalin ion interventi of rate]log[death ln

effecton interventi

oninterventix

ln

Hospital standardised mortality ratio

HSMR

• Definition – The ratio of actual number of deaths to

expected number of deaths in the hospital

Expected number of deaths

k

iij xββ

P

P10ln

• Original HSMR• X

– Age in year – Sex– Admission category

• Emergency versus elective

– Length of stay – Diagnosis group

• Account for 80% of death

– Co-morbidity• Chalson’s index • Might be able to use AIS scores

– Transfer• Patient was transferred from acute care

Step of analysis

– Fit logistic regression with death as the outcome – Estimate probability of death from the logit

model

– E = sum(p)

k

jjj

k

jjj

Xββ

Xββ

k

jjj

e

e

Xββ

Yp

1

0

10

1

exp1

1)1(

10

Modified HSMR• age in year • sex• Length of stay • Admission category

– Emergency vs elective • Transfers

– Acute care

• Diagnosis group – Account for 80% of death

• Co-morbidity– Chalson’s index

• age in year • sex• Length of stay • Patient transferring

– Ambulance – Non-ambulance

AIS scores Add• Risk behavior

• Alcohol • Transquilizer/sedation

• Type of trauma

Un-audited-hospital

Audited-hospital

HSMR=1

01

002

003

004

005

006

00H

SM

R

1999 2000 2001 2002 2003 2004 2005

YearHSMR= Actual death/Expected death x100

Hospital standardised mortality ratio by Audited-unaudited hospitals and years

Problem

• Missing – Diagnosis – Co-morbid– Length o stay

• Data validation??