1. 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.thraatk@mahidol.ac.th

2. 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

3. Type of meta-analysis Summary data –Unit of analysis is study –Mean (SD) –Count/frequency data by intervention & outcome –Person-time data

4. Summary-data Continuous data StudyiNMeanSD Rx/Exp+N1Mean1SD1 Cont/Exp-N2Mean2SD2

5. Summary-data Categorical data StudyiCaseControl Rx/Exp+ Cont/Exp- acac bdbd

6. 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

7. 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

8. 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

9. 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

10. Data collection & management –Data Registry Databases Data coding Data entry Cleaning Checking Validate data QC Hospitals Data manager Validated Data

11. Retrieve databases Combine data Re-check data Analyse data Statistician Report results Writing report (manuscript) Publish (annual, twice/year)

12. Data analysis Heterogeneity test –Different source data are homogeneous? Homogeneity

13. Analysis Heterogeneity

14. Outcomes Death/alive Disability/Non-disability Complications –Infection –Fracture Hospitalization Hospital days QoL Cost

15. Count (discrete) outcome Poisson regression –Number of death –Number of infection –Number of disability –Number of fracture InterventionPeriodPopNo. of death Death /1000 RR Audited2001-2005878703926450.65 (0.62, 0.67) Non-audited1999-200322824313120571

17. Hospital standardised mortality ratio

18. HSMR Definition –The ratio of actual number of deaths to expected number of deaths in the hospital

19. Expected number of deaths

20. 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

21. Step of analysis –Fit logistic regression with death as the outcome –Estimate probability of death from the logit model –E = sum(p)

22. 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

24. Problem Missing –Diagnosis –Co-morbid –Length o stay Data validation??