Bayesian Trial Designs: Drug Case Study

Bayesian Trial Designs: Drug Case Study Donald A. Berry dberry@mdanderson.org

Outline • Some history • Why Bayes? • Adaptive designs • Case study

2004 JHU/FDA Workshop:“Can Bayesian Approaches to Studying New Treatments Improve Regulatory Decision-Making?” www.prous.com/bayesian2004 www.cfsan.fda.gov/~frf/ bayesdl.html

Upcoming in 2005 • Special issue of Clinical Trials • “Bayesian Clinical Trials”Nature Reviews Drug Discovery

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

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

PREDICTIVE PROBABILITIES • Critical component of experimental design • In monitoring trials

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

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

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

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

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

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

Drop TI Compare n = 75

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

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

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

Recommendation to DSMB to • Stop for superiority if Q ≥ 0.99 • Stop accrual for futility if P(pE – pS < 0.10|data) > PF • PF depends on current n . . .

Common prior density for pE & pS • Independent • Reasonably non-informative • Mean = 0.30 • SD = 0.20

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

Q = 0.79

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

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

One simulation; pS = 0.30, pE = 0.45 Superiority boundary Final Std 12/38 19/60 20/65 Exp 38/83 82/167 87/178

One simulation; pE = pS = 0.30 Futility boundary 9 mos.EndFinal Std 8/39 15/57 18/68 Exp 11/42 32/81 22/87

Operating characteristics

FDA: Why do this? What’s the advantage? • Enthusiasm of patients & investigators • Comparison with standard design . . .

Adaptive vs tailored balanced design w/same false-positive rate& power (Mean number patients by arm)

FDA: • Use flat priors • Error size to 0.025 • Other null hypotheses • We fixed all … & willing to modify as necessary

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

Outline • Some history • Why Bayes? • Adaptive designs • Case study

Bayesian Trial Designs: Drug Case Study