1 / 24

Applying meta-analysis to trauma registry

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.

trula
Download Presentation

Applying meta-analysis to trauma registry

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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.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 Studyi N Mean SD Rx/Exp+ N1 Mean1 SD1 Cont/Exp- N2 Mean2 SD2

  5. Summary-data • Categorical data

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

  11. Retrieve databases Combine data Statistician Re-check data Analyse data 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

  16. Hospital standardised mortality ratio

  17. HSMR • Definition • The ratio of actual number of deaths to expected number of deaths in the hospital

  18. Expected number of deaths

  19. 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

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

  21. 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

  22. Problem • Missing • Diagnosis • Co-morbid • Length o stay • Data validation??

More Related