Overview of Trial Design Options: Adults (Challenges in Clinical Trials in People for Whom Available Therapies have Fail

1. Overview of Trial Design Options: Adults(Challenges in Clinical Trials in People for Whom Available Therapies have Failed ) Martin Schechter Canadian HIV Trials Network University of British Columbia

2. WARNING • more questions than answers ahead

3. Randomized Controls or Not?

4. Tuberculous Meningitis • universally fatal prior to 1945 • 1946 - streptomycin - new drug in short supply • some treated patients survived • randomized controls unnecessary

5. Tuberculous Meningitis • extremely homogeneous patient group • mortality outcomes • prior outcome pattern fully characterized • short term study (avoid secular trends) • adherence not an issue

7. Patient Heterogeneity

8. Increasing Heterogeneity • drug history • drug exposure intensity • genotype • phenotype • virological status • immunological status • clinical status

9. Increasing Heterogeneity 2 • toxicities • malabsorption • previous treatment interruptions • adherence • attitude about treatment • unknown confounders

10. Increasing Heterogeneity 3 • heterogeneity per se does not matter • heterogeneity matters if the variables are strongly prognostic

11. Selected variables predicting decline of pVL < 400 (Multi-drug Rescue Therapy - Montaner et al) * baseline CD4 and resistance to DDI, DLV, IND, NLV, NVP, RIT, SAQ were not significant

12. Patient Heterogeneity

13. Ability to Control Confounders Patient Heterogeneity

14. Salvage Studies Live Here

15. Can we avoid randomized trials? • historical controls? • a very attractive approach • lessons from the history of medicine

16. Gastric Freezing for DU • President of ACS - cooled gastric balloons • case series very impressive • “Since 1961, no patients with duodenal ulcer referred for elective operation have been operated on in the senior author’s service. This circumstance itself bespeaks the confidence in the method by patients as well as surgeons.”

17. Gastric Freezing for DU • led to sale of 2500 gastric freeze machines • an estimated 15,000 patients chilled • double blind RCT in late 1960’s • Outcome = surgery, bleed or intractable pain • Sham group 44% • Freeze group 51%

18. VA Study - Estrogen & Prostate Ca • RCT of 2313 patients recruited over 7 years • no change in eligibility criteria throughout • placebo patients in first 2.5 years had significantly worse survival than estrogen patients in the last 2.5 years

19. Uncontrolled Phase II Cancer studies • uncontrolled series in advanced bowel cancer • 20 different cases series of rapid injection 5-FU • among 6 largest (n= 40 to 150) • response rates ranged from 11% to 55%

20. Traditional Orthodoxy • well known that historical control studies are far more likely to yield positive results • Sacks HS, Chalmers TC, Smith H Jr. Sensitivity and specificity of clinical trials. Randomized v historical controls. Arch Int Med 1983; 143(4): 753-5. • dominance of the RCT • ongoing debate

21. Role of Observational Data • Concato J, Shah N, Horwitz RI, Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 2000; 342:1887-92. RCT case-control cohort

22. Role of Observational Data • well designed case-control and cohort studies are not the same as historical control studies • former involve careful selection of controls • concurrent • could play a role if prognostic variables completely characterized

23. Non-randomized concurrent comparisons by post-randomization variables • within-study comparisons based on adherence to a regimen • if adherers fare better, is this not proof of efficacy?

24. Analysis by post-randomization adherence in a lipid lowering trial Medication A * adjusted for 40 baseline predictors of CV mortality

25. Analysis by post-randomization adherence in a lipid lowering trial Placebo * adjusted for 40 baseline predictors of CV mortality

26. Salvage Therapy and Non-randomized Controls • very heterogeneous populations • some variables measurable, some not • strong prognostic factors • many factors as yet unidentified • variable surrogate marker outcome • CAUTION!

27. Salvage Therapy and Non-randomized Controls • homogeneous definition • “worst” category across the board • closer to true “salvage” definition • well characterized outcomes

28. Control of Confounders • randomization • large sample size • combination therapy of randomization and large sample size • likely to distribute known and unknown confounders equally

29. Remedies for Confounding in Smaller Sample Sizes • stratified randomization • 2n strata unwieldy • risk index • minimization • adaptive allocation made to minimize imbalances • post-hoc adjustment • effect on unknown confounders • crude vs. multivariate

30. There is no within-study remedy for lack of power in smaller sample size studies!

31. The Need for Blinding? • it is orthodoxy that blinding is required • numerous studies have shown • less bias in fully blinded studies • lower likelihood of positive results

32. The Need for Blinding? based on 33 meta-analyses of 250 primary trials involving a total of 62,091 participants and 12,030 outcome events. Schulz KF, Chalmers I, Hayes R, Altman DG. Empirical Evidence of Bias: Dimensions of Methodological Quality Associated With Estimates of Treatment Effects in Controlled Trials. JAMA 1995;273:408-412.

33. The Need for Blinding? • Is blinding feasible? • Can the artifact of blinding introduce more bias than it prevents?

34. The Need for Blinding - An example • Standard (≤ 4 drugs) vs. Mega-HAART (≥ 5) • Standard may benefit through adherence • Blinding would mean from 9 to 17 different types of pills • Could wipe out the adherence advantage of Standard-HAART • Bias from a clinical trial artifact

36. Treatment Received Intent to Treat

37. Particular Clinical Trial Challenges • rapid cross-over/drop-out • eg. cessation (multi-drug to interruption) • intent-to-treat becomes meaningless • treatment-received becomes biased • availability of new treatments or strategies • e.g. genotypic testing, compassionate access • when not built into current protocol

38. Clinical Trial Challenges • rapid cross-over/drop-out • offer of early vs. late Rx may induce better protocol adherence • e.g. interruption now vs. interruption in X mos • participant education - possibility of switch after poor response trigger • availability of new treatments or strategies • rolling protocols • pre-planned randomization of future options

39. Factorial Designs - Illustration • What is the role of ADV vs. DLV and NLF vs. RTV in NNRTI-naïve, IND treated subjects? SQV sgc + RTV + DLV SQV sgc + NLF + ADV

40. ACTG 359 (2 x 3 Factorial) SQV sgc +

41. OPTIMA (2 x 2 Factorial)

42. Randomised trial of effects of vitamin supplements on pregnancy outcomes and T cell counts in HIV-1-infected women in TanzaniaFawzi WW et al. Lancet 1998; 351:1477-82.

43. Stratified Factorial Combo 2 Combo 1 Factor present Combo 3 Combo 4 Combo 2 Combo 1 Factor absent Combo 3 Combo 4

44. Factorial Designs • Suppose Factor I, II, III (e.g. mutation) • Suppose Factor A, B, C • Three combinations 1, 2, 3 • Full factorial = 3 x 3 x 3 = 27 different cells • Latin Square design (basic science, vet science)

45. Latin Squares

46. Factorial Designs • ideally suited when multiple therapies exist that can be given in different combinations • ideally suited when different strategies can be combined • Rx combinations, interruptions, genotyping, adjunctive therapy, immunomodulators, complementary therapy

47. Factorial Designs • independent treatment effects and interactions • symmetrical and efficient • more bang for the buck • generally underutilized in clinical trial literature

48. Drug-wise vs. Strategy-wise Evaluation • Suppose new drugs A and B in 2 classes • new resistance patterns within class • A available for trials now • B available in 6 months

49. Drug-wise Evaluation • Trial 1 of switch A vs. non-switch • Later Trial 2 of switch B vs non-switch • Each could possibly contaminate the other • End result: 2 monotherapy switch trials with co-intervention

50. Strategy-wise Evaluation 1 • Start Trial 1 of switch A vs non-switch • Pre-schedule second randomization of switch B vs. non-switch when B is available