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Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power.

Jiaying Yang1, Guochun Li2, Dongqing Yang2

  • 1Department of Public Health, School of Medicine, Nanjing University of Chinese Medicine, 138 Xianlin Rd, Nanjing, 210023, China. yang_jy@foxmail.com.

BMC Medical Research Methodology
|January 17, 2024
PubMed
Summary
This summary is machine-generated.

A new seamless phase 2/3 clinical trial design using Bayesian predictive power (BPP) improves efficiency for trials with multiple co-primary endpoints (CPEs). This method enhances power and early stopping for futile trials compared to conditional power (CP).

Keywords:
Bayesian predictive powerCo-primary endpointsConditional powerSeamless phase 2/3 design

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Area of Science:

  • Clinical Trial Design
  • Biostatistics
  • Pharmaceutical Research

Background:

  • Seamless phase 2/3 clinical trials are increasingly adopted, particularly for single-endpoint studies.
  • Trials with multiple co-primary endpoints (CPEs) face challenges with inflated Type 2 error rates and large sample sizes.
  • Existing methods struggle to efficiently manage multiple CPEs in seamless designs.

Purpose of the Study:

  • To introduce and evaluate a novel seamless phase 2/3 design strategy using Bayesian predictive power (BPP).
  • To compare the performance of the BPP approach against a conditional power (CP) based method for multiple CPEs.
  • To assess the impact of BPP on overall power, sample size re-estimation, and early stopping for futility.

Main Methods:

  • Developed a seamless phase 2/3 design incorporating Bayesian predictive power (BPP) for interim futility monitoring and sample size re-estimation.
  • Utilized a Dirichlet-multinomial distribution to model correlations among multiple co-primary endpoints (CPEs).
  • Compared the BPP approach with a conditional power (CP) based alternative using a simulated seamless phase 2/3 vaccine trial with four binary endpoints.

Main Results:

  • The BPP approach demonstrated superior or comparable overall power to the CP approach, especially with smaller phase 2 sample sizes (e.g., 50 or 100 subjects).
  • With n1=50 and correlation ρ=0, BPP showed an 8.54% power advantage over CP.
  • For larger phase 2 sample sizes (e.g., 150 or 200), BPP exhibited greater efficiency in early stopping for futile trials, with a peak difference of 5.76% in early stop probability compared to CP when n1=200 and ρ=0. Both methods maintained Type 1 error rates below 2.5%.

Conclusions:

  • The proposed seamless phase 2/3 design integrating the Dirichlet-Multinomial model with Bayesian predictive power (BPP) offers significant advantages.
  • BPP provides improved power and efficiency in terminating futile trials for seamless designs with multiple co-primary endpoints compared to the CP approach.
  • This BPP-based strategy is a valuable advancement for optimizing clinical trial design in complex scenarios.