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Bayesian Trial Designs: Drug Case Study Donald A. Berry Donald A. Berry

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Presentation on theme: "Bayesian Trial Designs: Drug Case Study Donald A. Berry Donald A. Berry"— Presentation transcript:

1 Bayesian Trial Designs: Drug Case Study Donald A. Berry Donald A. Berry

2 2 2 Outline l Some history l Why Bayes? l Adaptive designs l Case study l Some history l Why Bayes? l Adaptive designs l Case study

3

4 JHU/FDA Workshop: Can Bayesian Approaches to Studying New Treatments Improve Regulatory Decision-Making? bayesdl.html bayesdl.html

5 5 5 Upcoming in 2005 l Special issue of Clinical Trials l Bayesian Clinical Trials Nature Reviews Drug Discovery l Special issue of Clinical Trials l Bayesian Clinical Trials Nature Reviews Drug Discovery

6 6 6 Selected history of Bayesian trials l Medical devices (30+) l 200+ at M.D. Anderson (Phase I, II, I/II) l Cancer & Leukemia Group B l Pharma n ASTIN (Pfizer) n Pravigard PAC (BMS) n Other l Decision analysis (go to phase III?) l Medical devices (30+) l 200+ at M.D. Anderson (Phase I, II, I/II) l Cancer & Leukemia Group B l Pharma n ASTIN (Pfizer) n Pravigard PAC (BMS) n Other l Decision analysis (go to phase III?)

7 7 7 Why Bayes? l On-line learning (ideal for adapting) l Predictive probabilities (including modeling outcome relationships) l Synthesis (via hierarchical modeling, for example) l On-line learning (ideal for adapting) l Predictive probabilities (including modeling outcome relationships) l Synthesis (via hierarchical modeling, for example)

8 8 8 PREDICTIVE PROBABILITIES l Critical component of experimental design l In monitoring trials l Critical component of experimental design l In monitoring trials

9 9 9 Herceptin in neoadjuvant BC l Endpoint: tumor response l Balanced randomized, H & C l Sample size planned: 164 l Interim results after n = 34: n Control: 4/16 = 25% n Herceptin: 12/18 = 67% l Not unexpected (prior?) l Predictive probab of stat sig: 95% l DMC stopped the trial l ASCO and JCOreactions … l Endpoint: tumor response l Balanced randomized, H & C l Sample size planned: 164 l Interim results after n = 34: n Control: 4/16 = 25% n Herceptin: 12/18 = 67% l Not unexpected (prior?) l Predictive probab of stat sig: 95% l DMC stopped the trial l ASCO and JCOreactions …

10 10 ADAPTIVE DESIGNS: Approach and Methodology l Look at the accumulating data l Update probabilities l Find predictive probabilities l Use backward induction l Simulate to find false positive rate and statistical power l Look at the accumulating data l Update probabilities l Find predictive probabilities l Use backward induction l Simulate to find false positive rate and statistical power

11 11 Adaptive strategies l Stop early (or late!) n Futility n Success l Change doses l Add arms (e.g., combos) l Drop arms l Seamless phases l Stop early (or late!) n Futility n Success l Change doses l Add arms (e.g., combos) l Drop arms l Seamless phases

12 12 Goals l Learn faster: More efficient trials l More efficient drug/device development l Better treatment of patients in clinical trials l Learn faster: More efficient trials l More efficient drug/device development l Better treatment of patients in clinical trials

13 13 l Troxacitabine (T) in acute myeloid leukemia (AML) combined with cytarabine (A) or idarubicin (I) l Adaptive randomization to: IA vs TA vs TI l Max n = 75 l End point: Time to CR (< 50 days) l Troxacitabine (T) in acute myeloid leukemia (AML) combined with cytarabine (A) or idarubicin (I) l Adaptive randomization to: IA vs TA vs TI l Max n = 75 l End point: Time to CR (< 50 days) ADAPTIVE RANDOMIZATION Giles, et al JCO (2003)

14 14 Adaptive Randomization l Assign 1/3 to IA (standard) throughout (until only 2 arms) l Adaptive to TA and TI based on current probability > IA l Results l Assign 1/3 to IA (standard) throughout (until only 2 arms) l Adaptive to TA and TI based on current probability > IA l Results

15 15

16 16 Compare n = 75 Drop TI

17 17 Summary of results CR < 50 days: n IA:10/18 = 56% n TA: 3/11 = 27% n TI: 0/5 = 0% Criticisms... CR < 50 days: n IA:10/18 = 56% n TA: 3/11 = 27% n TI: 0/5 = 0% Criticisms...

18 18 Consequences of Bayesian Adaptive Approach l Fundamental change in way we do medical research l More rapid progress l Well get the dose right! l Better treatment of patients l... at less cost l Fundamental change in way we do medical research l More rapid progress l Well get the dose right! l Better treatment of patients l... at less cost

19 19 CASE STUDY: PHASE III TRIAL l Dichotomous endpoint l Q = P(p E > p S |data) l Min n = 150; Max n = 600 l 1:1 randomize 1st 50, then assign to arm E with probability Q n Except that 0.2 P(assign E) 0.8 l Dichotomous endpoint l Q = P(p E > p S |data) l Min n = 150; Max n = 600 l 1:1 randomize 1st 50, then assign to arm E with probability Q n Except that 0.2 P(assign E) 0.8 Small company!

20 20 Recommendation to DSMB to l Stop for superiority if Q 0.99 l Stop accrual for futility if P(p E – p S PF n PF depends on current n... l Stop for superiority if Q 0.99 l Stop accrual for futility if P(p E – p S PF n PF depends on current n...

21 21 PF

22 22 Common prior density for p E & p S l Independent l Reasonably non-informative l Mean = 0.30 l SD = 0.20 l Independent l Reasonably non-informative l Mean = 0.30 l SD = 0.20

23 23

24 24 Updating After 20 patients on each arm n 8/20 responses on arm S n 12/20 responses on arm E After 20 patients on each arm n 8/20 responses on arm S n 12/20 responses on arm E

25 25 Q = 0.79

26 26 Assumptions l Accrual: 10/month l 50-day delay to assess response l Accrual: 10/month l 50-day delay to assess response

27 27 Need to stratify. But how? Suppose probability assign to experimental arm is 30%, with these data...

28 28

29 29 One simulation; p S = 0.30, p E = 0.45 Final Std12/38 19/60 20/65 Exp38/83 82/16787/178 Superiority boundary

30 30 9 mos. End Final Std 8/39 15/57 18/68 Exp 11/42 32/81 22/87 One simulation; p E = p S = 0.30 Futility boundary

31 31 Operating characteristics

32 32 FDA: Why do this? Whats the advantage? l Enthusiasm of patients & investigators l Comparison with standard design... l Enthusiasm of patients & investigators l Comparison with standard design...

33 33 Adaptive vs tailored balanced design w/same false-positive rate & power (Mean number patients by arm) ORR Arm p S = 0.20 p E = 0.35 p S = 0.30 p E = 0.45 p S = 0.40 p E = 0.55 StdExpStdExpStdExp Adaptive Balanced Savings

34 34 FDA: l Use flat priors l Error size to l Other null hypotheses l We fixed all … & willing to modify as necessary l Use flat priors l Error size to l Other null hypotheses l We fixed all … & willing to modify as necessary

35 35 The rest of the story … l PIs on board l CRO in place l IRBs approved l FDA nixed! l PIs on board l CRO in place l IRBs approved l FDA nixed!

36 36 Outline l Some history l Why Bayes? l Adaptive designs l Case study l Some history l Why Bayes? l Adaptive designs l Case study


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