Presentations in this series Introduction Self-matching Proxies Intermediates Instruments

1. Avoiding Bias Due toUnmeasured Covariates Presentations in this series Introduction Self-matching Proxies Intermediates Instruments Equipoise Alec Walker

2. X T D

3. X Randomization T D

4. X Randomization Self-matching T D

5. X Randomization Self-matching T D Proxies Proxies

6. X Randomization Self-matching T D Proxies Proxies Intermediates Intermediates

7. A Cautionary Example:Vaccination for influenza is associated with a reduction in the apparent risk of almost every subsequent serious health event.

8. Group Health Cooperative of Puget Sound, 1995-2002 “We evaluated a cohort of 72527 persons 65 years of age and older followed during an 8 year period and assessed the risk of death from any cause, or hospitalization for pneumonia or influenza, in relation to influenza vaccination, in periods before, during, and after influenza seasons. Secondary models adjusted for covariates defined primarily by diagnosis codes assigned to medical encounters.” Jackson LA, Jackson ML, Nelson JC, Neuzil KM, Weiss NS. Evidence of bias in estimates of influenza vaccine effectiveness in seniors. International Journal of Epidemiology 2006;35:337–344

9. Vaccinees had lower risk than nonvaccinees of hospitalization for pneumonia or influenza during and after the influenza season, as expected. . ... . . ... . .. . . . . .

10. Vaccinees had lower risk than nonvaccinees of hospitalization for pneumonia or influenza during and after the influenza season, as expected. .. . . . . . Vaccinees also had lower risk than non-vaccinees before the influenza season began.

11. Vaccinees had lower risk than nonvaccinees of hospitalization for pneumonia or influenza during and after the influenza season, as expected. This pre-season reduction cannot have been a causal effect of vaccination. .. . . . . . Vaccinees also had lower risk than non-vaccinees before the influenza season began.

12. Vaccinees had lower all-cause mortality than non-vaccineesbefore, during and after flu season.

13. Adjusting for many baseline factors • atrial fibrillation, heart disease, lung disease, diabetes mellitus, dementia, renal disease, cancer, vasculitis/rheumatologic disease, hypertension, lipid disorders, pneumonia hospitalization in previous year, and 12+ outpatient visits • slightly magnified the bias. • Some of the controlled factors may have been instruments, resulting in Z-bias with respect to unmeasured confounders.

14. General good health Baseline risk factors Routine preventive care Influenza Vaccine (Low) Mortality

15. Confounder with a mediated effect on disease C I E D

16. Post-treatment intermediates Cancer Anticipation of future treatments Immunosuppressive treatments Vaccine Death time

17. Control for Post-treatment intermediates Cancer Anticipation of future treatments Immunosuppressive treatments Vaccine Death time

18. Control for Post-treatment intermediates Cancer Anticipation of future treatments Immunosuppressive treatments Vaccine Death time

19. Control for Post-treatment intermediates Cancer Ignorance of the indications for therapy may justify controlling for a “downstream” time-varying covariate. Anticipation of future treatments Immunosuppressive treatments Vaccine Death time

20. Intermediate Variables for Predictors of Treatment

21. Propensity models are typically constructed in such a way as to capture proxies for predictors of treatment (previous lecture)

22. Propensity models are typically constructed in such a way as to capture proxies for predictors of treatment (previous lecture) • They also captureintermediates U I T D

23. Propensity models are typically constructed in such a way as to capture proxies for predictors of treatment (previous lecture) • They also captureintermediates U I T D

24. Intermediate Variables – Summary • Blocking the a variable on a unique causal path from a confounder to either • Outcome or • Treatment is sufficient to block the confounding effect • In medicine we almost never know what a doctor is thinking about a patient, but we do often know his or her actions. These are intermediate variables on the pathways that tie • Diagnosis • Prognosis, and • Treatment • Events that follow after treatment are not necessarily intermediates, and should be controlled if they are intermediates for unmeasured confounders.

25. Avoiding Bias Due toUnmeasured Covariates Presentations in this series Overview and Randomization Self-matching Proxies Intermediates Instruments Alec Walker