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Bayesian Phase II optimization for time-to-event data based on historical information.

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Statistical Methods in Medical Research
|December 30, 2017
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Summary
This summary is machine-generated.

Optimizing drug development decisions, this study enhances proof-of-concept trials by incorporating historical data. New criteria focus on treatment effect size for better decision-making in confirmatory trials.

Keywords:
BayesGo–NoGo decisionProof-of-conceptmeta-analytic-predictive prior distributionoperating characteristicstime-to-event

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmaceutical Development

Background:

  • Drug development requires crucial proof-of-concept decisions after Phase II trials.
  • Limited efficacy data complicates the choice between initiating confirmatory trials or not.

Purpose of the Study:

  • To optimize the design of proof-of-concept trials for improved decision-making.
  • To develop pre-specified decision criteria that account for uncertainty in treatment effects.

Main Methods:

  • Incorporated historical control information into trial design.
  • Utilized time-to-event data in a randomized 2-arm trial framework.
  • Defined proof-of-concept criteria based on the posterior distribution of the hazard ratio, using treatment effect size over statistical significance.

Main Results:

  • Developed criteria optimized for sensitivity and specificity using historical data.
  • Employed exponential modeling for event times and conjugate prior-to-posterior calculations.
  • Optimized design parameters like sample size and allocation ratio to maximize correct decision probabilities.

Conclusions:

  • The proposed method provides a robust framework for proof-of-concept trial design.
  • Incorporating historical data and focusing on effect size enhances decision accuracy in drug development.
  • The approach was successfully illustrated with a lung cancer clinical trial example.