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Sample size re-estimation in Phase 2 dose-finding: Conditional power versus Bayesian predictive power.

Qingyang Liu1, Guanyu Hu2, Binqi Ye3

  • 1Department of Statistics, University of Connecticut, Storrs, Connecticut, USA.

Pharmaceutical Statistics
|November 23, 2022
PubMed
Summary
This summary is machine-generated.

Unblinded sample size re-estimation (SSR) improves clinical trial efficiency by adjusting sample size based on interim data. This study introduces adaptive designs using frequentist and Bayesian methods for dose-finding studies, ensuring robust statistical power.

Keywords:
adaptive designclinical trialcontrast testprior informationsample size re-estimationtype I error

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Area of Science:

  • Clinical Trial Design and Methodology
  • Biostatistics
  • Pharmacometrics

Background:

  • Unblinded sample size re-estimation (SSR) is crucial in clinical trials with high uncertainty regarding treatment effects.
  • Phase II dose-finding studies often employ Proof of Concept (PoC) designs, necessitating efficient statistical approaches.
  • Leveraging information across multiple treatment arms is key for robust dose selection.

Purpose of the Study:

  • To propose novel two-stage sample size re-estimation (SSR) designs for Phase II clinical trials.
  • To evaluate both frequentist conditional power (CP) and Bayesian predictive power (PP) for adaptive trial designs.
  • To demonstrate the application of these methods for single and multiple contrast tests in dose-finding studies.

Main Methods:

  • Development of two-stage adaptive designs incorporating unblinded SSR.
  • Utilizing frequentist conditional power (CP) and Bayesian predictive power (PP) for sample size adjustments.
  • Application to single and multiple contrast tests, including Bayesian SSR with flexible prior settings.
  • Rigorous protection of Type I error rates throughout the adaptive process.

Main Results:

  • Proposed designs effectively handle uncertainty in treatment effect estimation.
  • Bayesian SSR allows incorporation of diverse prior knowledge, enhancing flexibility.
  • Simulation studies confirm the advantages of unblinded SSR in multi-arm trials.
  • Adaptive strategies maintain statistical integrity while optimizing sample size.

Conclusions:

  • Unblinded SSR, utilizing CP and PP, offers a robust framework for adaptive Phase II dose-finding studies.
  • The proposed Bayesian SSR provides a flexible approach adaptable to various prior beliefs.
  • These adaptive designs ensure Type I error control and demonstrate improved efficiency in multi-arm settings.