1. Presentations in this series 1.Introduction 2.Self-matching 3.Proxies 4.Intermediates 5.Instruments 6.Equipoise Avoiding Bias Due to Unmeasured Covariates Alec Walker

2. TD X

3. TD X Randomization

4. TD X Self-matching

5. TD X Proxies Randomization

6. TD X Self-matching Proxies Randomization Intermediates

7. 7

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

10. 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. 11 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. This pre-season reduction cannot have been a causal effect of vaccination.

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

13. 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. 14 Influenza Vaccine (Low) Mortality Routine preventive care General good health Baseline risk factors

15. 15 E D C I

16. 16 Vaccine Death Anticipation of future treatments Immunosuppressive treatments Cancer time

17. 17 Vaccine Death Anticipation of future treatments Immunosuppressive treatments Cancer time

18. 18 Vaccine Death Anticipation of future treatments Immunosuppressive treatments Cancer time

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

20. 20

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

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

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

24. 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. 24

25. Presentations in this series 1.Overview and Randomization 2.Self-matching 3.Proxies 4.Intermediates 5.Instruments Avoiding Bias Due to Unmeasured Covariates Alec Walker 25