1. Measurement and Modeling Issues with Adherence to Pharmacotherapy M. Christopher Roebuck, M.B.A. Director, Health Economics CVS Caremark Teresa B. Gibson, Ph.D. Director, Health Outcomes Thomson Reuters, Healthcare & Science AMCP Educational Conference Workshop (W1) St. Louis, Missouri Thursday, October 14, 2010 08:15-09:30

4. Measurement Issues

11. PDC & Hospitalization: Functional Form

12. PDC & Hospitalization: Functional Form

13. PDC & Hospitalization: Functional Form

17. Comparing PDC Approaches: Hypertension

18. Comparing PDC Approaches: Diabetes

19. Comparing PDC Approaches: Dyslipidemia

21. Modeling Issues

24. Adherence and Outcomes Randomization relies on law of large numbers to create like comparison groups to compare means, and estimate the effects of adherence Adherent Non-Adherent Outcome (e.g., # hospitalizations)

25. Adherence and Outcomes With non-randomized data, there may be differences between those who are adherent and non-adherent Adherent Non-Adherent Outcome (e.g., # hospitalizations)

30. Regression Adjusted Example: Results Rates are similar (p=0.5821) Adjusted OR: 0.861 (p=0.073) Rate of Acute Myocardial Infarction (N=55,555)

36. Propensity Score Matching Example: Results Rates are different (p<0.01) Rate of Acute Myocardial Infarction (N=30,190) Adjusted OR: 0.791 (p=0.045)

37. Propensity Score Matching Caliper Matching Match within common support

40. Adherence and Outcomes X Z X: covariates Z: instrumental variables Y=outcome Adherent Outcome (Y) (e.g., # hospitalizations)

44. Instrumental Variables Example: Results Rate of Acute Myocardial Infarction (N=55,555)