1. Adaptive Strategies in Drug Abuse Research Carl Pieper & Janet Levy Steering Committee Conference Steering Committee Conference March 22, 2007

2. Introduction

3. Goals: Inform about Adaptive Strategies (assume no prior knowledge) Not use a lot of math Note the limitations in design Show appropriate and inappropriate uses

4. History Used in Drug trials & Early Phase Cancer Trials STAR*D trial If a patient fails to respond, we want to try another intervention as quickly as possible.

5. Design is useful when ‘response’ is known quickly in the trial. Response=Outcome or Compliance.

6. STAR*D ‘After unsuccessful treatment with an SSRI, approximately 1 in 4 patients had a remission of symptoms after switching to another antidepressant. Any one of the medications in the study provided a reasonable second-step choice for patients with depression’ NEJM, 354, 12, p. 1231.

7. –References: –Murphy(2001), Lavori(2001), Lavori(2004), Murphy(2005), Rush (2006). –A framework for: tailoring an individual patient’s therapy to his/her responses to previous therapies tailoring an individual patient’s therapy to his/her responses to previous therapies managing a patient’s condition over time –Applicable to the treatment of chronic conditions –Applicable to Exercise/Drug/Diet interventions –Mimic clinical practice (change until something works).

8. Example: CLASSIC Drug trial Base line Time Randomize treatment Placebo Assessment

9. Issues: 1) You often know early in trial who is: a) Compliant b) had effective treatment c) ‘Response’ 2) May be unethical to have a subject remain on a treatment that is not working. 3) You may want to change intervention based on interim data points (Either Response or Compliance) or new information.

10. New treatment/randomization based on: 1) Compliance 2) Response to date 3) Other External Criteria.

11. Adaptive Strategy Base- line Randomize Compliant Non-Compliant Treatment Placebo randomize Reduce treatment Same treatment Increase treatment Same treatment Compliant Non-compliant Measure final Outcome randomize

12. Here ‘compliance’ defines second level. ‘Response’ or some other criteria could also be employed.

13. Uses: 1) When a lot of strategies are to be compared 2) When interactions are to be considered.

14. Notice this is different from randomizing subjects at beginning of trial to 1 of 6 arms. We do not know final group assignment until middle of trial (when compliance known). In a drug trial, we could offer change in intervention only to those who fail, allowing those who succeed to remain on drug.

15. Re-randomizing guards against getting very small numbers in cells of interest. Guarantees, subjects of interest will get treatment of interest. (e.g. it may not make sense to give responders MORE treatment & unethical to give non- responders LESS treatment)

16. Benefits: The benefits of the method are: 1) Efficiency 2) Ethics 3) Allowing one to ‘tailor’ intervention, Remove subjects early from an ineffective Intervention, or Let one try different combinations of Intervention. Remove subjects early from an ineffective Intervention, or Let one try different combinations of Intervention.

17. Costs (part 1) 1) Comparisons – You may not get to make the comparisons you might initially think you want.

18. Base- line Randomize Compliant Non-Compliant Treat- ment Placebo randomize Reduce treatment Same treatment Increase treatment Same treatment Compliant Non-compliant Measure final Outcome A B C D E F Group F vs, C? F vs. D? A vs. E? B vs. E? - NO! randomize

19. Can treatment in the compliant be compared to a non-compliant? A vs. C A vs. D B vs. C B vs. D. E vs F - Compliance Effect. NO. Any ‘treatment’ effects truly be due to Compliance? And any ‘compliance’ effects May be due to treatment? So, the one exception may be B vs. D. where ‘same’ treatment was given. But this is a compliance Effect.

20. Can compliant be compared to compliant? Non-compliant to Non-compliant? Reason: Reason: F vs. C? F vs. D? A vs. E? B vs. E? F vs. C+D? E vs. A+ B? Non-compliant to treatment may not be non- compliant to Placebo. Groups are not comparable.

21. We have to take into account the randomization grouping factors may differ by Arm. E.g. ‘compliance’ in the placebo arm may not be ‘compliance’ in the intervention arm.

22. ‘Strategies’ In order to compare, we have to define effect at level 1. To know what the effect of ‘treatment’ is, it will have to be composed of the compliant + the non-compliant. To know what the effect of ‘treatment’ is, it will have to be composed of the compliant + the non-compliant.

23. Here we have 5 ‘strategies’. A+CA+DB+CB+D E + F - 1 Placebo Strategy. 4 Treatment Strategy (1 compliant group + 1 non-complaint group)

24. Each Treatment Strategy can be compared to the Placebo Strategy. Murphy (2004) – used this in a phase 2 method to find ‘best’ treatment. One hypothesis – the Maximum Strategy Mean in 1 arm > the Maximum Strategy Mean in the other arm

25. Strategies in this sense are thus interactions. E.g. If the maximal Treatment strategy is A+D, then for compliant we would want to reduce treatment, but for noncompliant we would want treatment to remain the same.

26. Murphy (2005, p. 1466). z = (√(n))*(strategy mean a – strategy mean b ) --------------------------------------------------------------------------- --------------------------------------------------------------------------- sqrt of [(variance of (√(n))*sample strategy mean a) + sqrt of [(variance of (√(n))*sample strategy mean a) + (variance of (√(n))*sample strategy mean b )] (variance of (√(n))*sample strategy mean b )]

27. Calculation of the ‘Mean’ is particular. It is a WEIGHTED MEAN, where the weights are defined by the sampling probabilities. (e.g. Assume 90% of the subjects are compliant, your strategy mean would be:.9* mean of compliant +.1* mean of Noncompliant ) (assuming equal randomizing probabilities).

28. Variance estimate of a strategy mean has 2 components: 1) Average variance within a group (e.g. within A & C), and, 2) Variance between groups (A vs. C). This we get from general ANOVA concepts. Total Variance = Variance Within + Variance Between Variance Within + Variance Between

29. We can test groups randomized at level 2. e.g. A vs. B Test of different therapy in Compliers C vs. D. Test of different therapy in Non-compliers C vs. D. Test of different therapy in Non-compliers

30. Since there are so many groups, tests must control for Type-I error.

31. Costs (part 2) Sampling Ratios: As the level of ‘compliance’ differs between arms, to maximize strategy comparisons, we may not use 50:50 sampling ratios. - This does not sit well with many clinicians. - Requires good knowledge of compliance structure.

32. For example, if the expected compliance is.75 in the treatment arm and.5 in the Placebo, then, we would sample at: So, for this example: “expected value of the number of treatment alternatives, regardless of response to the first stage treatment, and who received first stage treatment equal to Placebo” =[(1/( ((.75)*(2)) + ((.25)*(2) )]- 1 = [1/(1.5 +.5)]- 1 = 2 For assignment to Placebo, =[(1/( ((.5)*(1)) + ((.5)*(1)) )]- 1 =1. Then probability of assignment to enhanced arm =2/(2+1)=.67 Then probability of assignment to standard arm = 1./(2+1) =.33

33. So, we try to maximize the Strategy Mean comparison, not the mean of any 1 group. If we bifurcate in one group, we will place more subjects in that group.

34. Costs (3) Since there are inherently multiple tests, there is a type-I error rate to be addressed.

35. Summary Adaptive strategies for treating ongoing chronic conditions adjust treatments according to the patient’s previous treatments and responses. Trials to inform their development use randomization to support causal inferences regarding differences between alternative strategies or certain aspects of alternative strategies. There are many ways in which trials can be designed, depending upon the question asked by the clinical scientist.

36. Adaptive Strategies may let us: 1) Try different combinations based on Response or Compliance. 2) Allow one to test multiple combinations of intervention

37. Costs Our intervention is not 1 single thing, but rather multiple combinations of therapies. Because we are doing interaction detection, we have many arms  increased sample size Study will likely require replication testing the 2 ‘final’ strategies.

38. Math is only now being worked out. - Dissertation topics - Seminars & Talks at Stat. Meetings. - Design & Math effects being worked out now.

39. However, it requires us to make reasonable guesses about response/compliance rates. Costs Benefits may outweigh Costs. Design is also ‘Sexy’ – useful in grants.

40. Discussion & Thank You

41. References Lavori PW, Rush JA, Wisniewski SR,Alpert J, Fava M, Kupfer DJ, Nierenberg A, Quitkin FM, Sackeim HA, Thase ME, Trivedi M.(2001). Strengthening clinical effectiveness trials: equipoise-stratified randomization. Biological Psychiatry, 50: 792-801. Lavori, PW, Dawson, R.(2004) Dynamic treatment regimes: practical design considerations. Clinical Trials. 1: 9-20. Murphy, SA, Van Der Laan, MJ, Robins, JM and Conduct Problems Prevention Group.(2001). Marginal mean models for dynamic regimes. Journal of the American Statistical Association. 96, No. 456: 1410 – 1423. Murphy, SA. (2005). An experimental design for the development of adaptive treatment strategies. Statistics in Medicine.24:1455-1481. Rush AJ, Trivedi MH, Wisniewski SR, Stewart JW, Nierenberg AA, Thase ME, Ritz L, Biggs MM, Warden D, Luther JF, Shores-Wisldon K, Niederehe G, Fava M, STAR*D Study team. (2006). Bupropion-SR, Sertaline, or Venlafaxine-XR after failure of SSRIs for Depression. The New England Journal of Medicine. 354:12: 1231-1242.