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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

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bayesian trial designs drug case study

Bayesian Trial Designs: Drug Case Study

Donald A. Berry

dberry@mdanderson.org

outline
Outline
  • Some history
  • Why Bayes?
  • Adaptive designs
  • Case study
slide4
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
Upcoming in 2005
  • Special issue of Clinical Trials
  • “Bayesian Clinical Trials”Nature Reviews Drug Discovery
selected history of bayesian trials
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
Why Bayes?
  • On-line learning (ideal for adapting)
  • Predictive probabilities (including modeling outcome relationships)
  • Synthesis (via hierarchical modeling, for example)
predictive probabilities
PREDICTIVE PROBABILITIES
  • Critical component of experimental design
  • In monitoring trials
herceptin in neoadjuvant bc
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
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
Adaptive strategies
  • Stop early (or late!)
    • Futility
    • Success
  • Change doses
  • Add arms (e.g., combos)
  • Drop arms
  • Seamless phases
goals
Goals
  • Learn faster: More efficient trials
  • More efficient drug/device development
  • Better treatment of patients in clinical trials
adaptive randomization giles et al jco 2003
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
Adaptive Randomization
  • Assign 1/3 to IA (standard) throughout (until only 2 arms)
  • Adaptive to TA and TI based on current probability > IA
  • Results 
slide16

Drop

TI

Compare n = 75

summary of results
Summary of results

CR < 50 days:

  • IA: 10/18 = 56%
  • TA: 3/11 = 27%
  • TI: 0/5 = 0%

Criticisms . . .

consequences of bayesian adaptive approach
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
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
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 p e p s
Common prior density for pE & pS
  • Independent
  • Reasonably non-informative
  • Mean = 0.30
  • SD = 0.20
updating
Updating

After 20 patients on each arm

  • 8/20 responses on arm S
  • 12/20 responses on arm E
assumptions
Assumptions
  • Accrual: 10/month
  • 50-day delay to assess response
need to stratify but how
Need to stratify. But how?

Suppose probability assign to experimental arm is 30%, with these data . . .

one simulation p s 0 30 p e 0 45
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 p e p s 0 30
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

fda why do this what s the advantage
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
Adaptive vs tailored balanced design w/same false-positive rate& power (Mean number patients by arm)
slide34
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
The rest of the story …
  • PIs on board
  • CRO in place
  • IRBs approved
  • FDA nixed!
outline36
Outline
  • Some history
  • Why Bayes?
  • Adaptive designs
  • Case study