Overview of Adaptive Treatment Regimes

1. Overview of Adaptive Treatment Regimes Sachiko MiyaharaDr. Abdus Wahed

2. Before starting the presentation… Adaptive Experimental Design Adaptive Treatment Regimes ≠

3. Adaptive Treatment Regimes vs. Adaptive Experimental Design • Adaptive Treatment Regimes “…adaptive as used here refers to a time- varying therapy for managing a chronic illness”(Murphy,2005) • Adaptive Experimental Design “…such as designs in which treatment allocation probabilities for the present patients depend on the responses of past patients” (Murphy,2005)

4. Outline 1. What is Adaptive Treatment Regime? -Definition -Example -Objective 2. How to decide the best regime? - 3 different study designs - Comparison of 3 designs 3. Trial Example (STAR*D) 4. Inference on Adaptive Treatment Regimes

5. What is Adaptive Treatment Regime? Definition: a set of rule which select the best treatment option, which are made based on subjects’ condition up to that point.

6. What is Adaptive Treatment Regime? B1 Responder B1’ A B2 Non Responder B2’ Patient B1 Responder A’ B1’ B2 Non Responder B2’

7. 8 Possible Policies (1) Trt A followed by B1 if response, else B2 (AB1B2) (2) Trt A followed by B1 if response, else B2’ (AB1B2’) (3) Trt A followed by B1’ if response, else B2 (AB1’B2) (4) Trt A followed by B1’ if response, else B2’ (AB1’B2’) (5) Trt A’ followed by B1 if response, else B2 (A’B1B2) (6) Trt A’ followed by B1 if response, else B2’ (A’B1B2’) (7) Trt A’ followed by B1’ if response, else B2 (A’B1’B2) (8) Trt A’ followed by B1’ if response, else B2’ (A’B1’B2’)

8. What is the objective of the Adaptive Treatment Regimes? • Objective: To know which treatment strategy works the best, given a patient’s history.

9. What is the objective of the Adaptive Treatment Regime? A treatment naïve patient comes to a physician’s office. Questions: 1. What treatment strategy should the physician follow for that patient? 2. How should it be decided?

10. If one knew… (T be the outcome measurement) 1. E(T| AB1B2) = 15 2. E(T| AB1B2’) = 14 3. E(T| AB1’B2) = 18 4. E(T| AB1’B2’) = 17 5. E(T| A’B1B2) = 20 6. E(T| A’B1B2’) = 19 7. E(T| A’B1’B2) = 13 8. E(T| A’B1’B2’) = 12 Best Regime for the patient

11. In Reality… Problems: 1. E(T| . ) are not known (need to estimate) 2. How can one accurately and efficiently estimate E(T| . )?

12. How to estimate the expected outcome? Three study designs: 1. A clinical trial with 8 treatments 2. Combine existing trials 3. SMART (Sequential Multiple Assignment Randomized Trials)

13. Design 1: A clinical trial with 8 Treatment Policies AB1B2 AB1B2’ AB1’B2 AB1’B2’ Sample A’B1B2 A’B1B2’ A’B1’B2 A’B1’B2’ = Randomization

14. Design 2: Combining Existing Trials Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 B1 B1 B2 B2 A + + + + B1’ B1’ B2’ B2’ A’ Responder to A only Non Responder to A only Responder to A’ only Non Responder to A’ only

15. Design 3: SMART Sequential Multiple Assignment Randomized Trials (SMART) proposed by Dr. Murphy The SMART designs were adapted to: - Cancer (Thall 2000) - CATIE (Schneider 2001) – Alzheimer's Disease - STAR*D (Rush 2003) – Depression

16. Design 3: SMART B1 Responder B1’ A B2 Non Responder B2’ Sample B1 Responder A’ B1’ B2 Non Responder B2’ = Randomization

17. Comparisons of 3 Study Designs Yes Maybe Yes No No Yes Maybe Yes No

18. Sequenced Treatment Alternatives To Relieve Depression (STAR*D) 1.What is STAR*D? 2.The Study Design

19. What is STAR*D? • Multi-center clinical trial for depression • Largest and longest study to evaluate depression • N=4,041 • 7 years study period • Age between 18-75 • Referred by their doctors • 4 stages (3 randomizations)

20. STAR*D Study Design: Level 1 CIT Responder Eligible Subjects CIT Non Responder Go to Level 2

21. STAR*D Study Design: Level 2 CIT+BUP Add on CIT+BUS CIT+CT Lev 1 Non Responder BUP SER Switch VEN = Subject’s Choice CT = Randomization

22. STAR*D Study Design: Level 3 Lev3 Med+Li Add on Lev3 Med+Li Lev 2 Non Responder MIRT Switch NTP = Subject’s Choice = Randomization

23. STAR*D Study Design: Level 4 TCP Lev 3 Non Responder VEN+MIRT = Randomization

24. Details on Inference from SMART • Remember the goal is to estimate E(T|AB1B2) • First, how can we construct an unbiased estimator for E(T|AB1B2)?

25. Details on Inference from SMART • Let us ask ourselves, what would we have done if everyone in the sample were treated according to the strategy AB1B2 ? Responder B1 A Patient Non Responder B2

26. Details on Inference from SMART • What would we have done if everyone in the sample were treated according to the strategy AB1B2 ? Answer: E(T|AB1B2) = ΣTi/n Applies to 8-arm randomization trial

27. B1 Responder B1’ A B2 Non Responder B2’ Sample Details on Inference from SMART • But in SMART, we have not treated everyone with AB1B2

28. B1 Responder B1’ A B2 Non Responder B2’ Sample Details on Inference from SMART • Let C(AB1B2) be the set of patients who are treated according to the policy AB1B2

29. Details on Inference from SMART • We define R = Response indicator (1/0) Z1 = Treatment B1 indicator (1/0) Z2 = Treatment B2 indicator (1/0) Then C(AB1B2) = {i: [RiZ1i + (1-Ri)Z2i]=1}

30. Details on Inference from SMART One would define E(T|AB1B2) = Σ[RiZ1i + (1-Ri)Z2i]Ti/n’ Where n’ is the number of patients in C(AB1B2). This estimator would be biased as it ignores the second randomization.

31. Details on Inference from SMART • There are two types of patients in the set C(AB1B2) who were treated according to the policy AB1B2 A responder who received B1 and A nonresponder who received B2

32. B1 Responder B1’ A B2 Non Responder B2’ Sample Details on Inference from SMART

33. Details on Inference from SMART • Assuming equal randomization, A responder who received B1 was equally eligible to receive B1’ A responder who received B2 was equally eligible to receive B2’

34. Details on Inference from SMART • Thus A responder who received B1 in C(AB1B2) is representative of another patient who received B1’ and A non-responder who received B2 in C(AB1B2) is representative of another patient who received B2’

35. Details on Inference from SMART • We define weights as follows A responder who received B1 in C(AB1B2) receives a weight of 2 [1/(1/2)], also A non-responder who received B2 in C(AB1B2) receives a weight of 2 [1/(1/2)] While everyone else receives a weight of zero.

36. Details on Inference from SMART Unbiased estimator E(T|AB1B2) = Σ[RiZ1i + (1-Ri)Z2i]Ti/(n/2) And, in general, E(T|AB1B2) = Σ[RiZ1i /π1+ (1-Ri)Z2i /π2]Ti/n This estimator is unbiased under certain assumptions

37. Issues • Compare treatment strategies • Wald test possible but needs to derive covariance between estimators (which may not be independent of each other) • In survival analysis setting, how to derive formal tests to compare survival curves under different strategies • Is log-rank test applicable? • Can the proportional hazard model be applied here?

38. Issues • Efficiency issues • How can one improve efficiency of the proposed estimator • How to handle missing data (missing response information, censoring, etc.) • How to adjust for covariates when comparing treatment strategies • And most importantly,

39. Issues • Is it possible to tailor the best treatment strategy decisions to individual characteristics? • For instance, could we one day hand over an algorithm to a nurse (not physician) which would provide decisions like “If the patient is a caucacian female, age 50 or over, have normal HGB levels, bla bla bla…the best strategy for maintaining her chronic disease would be……..”

40. ATSRG link http://www.pitt.edu/~wahed/ATSRG/main.htm