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BASIC: A Bayesian adaptive synthetic-control design for phase II clinical trials.

Liyun Jiang1,2, Peter F Thall2, Fangrong Yan1

  • 1Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.

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|June 14, 2023
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Summary
This summary is machine-generated.

This study introduces a Bayesian adaptive synthetic control design, a hybrid trial method that uses historical data to reduce sample sizes. This approach offers similar power to randomized controlled trials with less bias than single-arm trials.

Keywords:
Bayesian adaptive designReal-world dataaugmented controlhistorical datarandomized controlled trials

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Area of Science:

  • Clinical trial design
  • Biostatistics
  • Health research methodology

Background:

  • Randomized controlled trials (RCTs) are effective but require large sample sizes.
  • Single-arm trials use smaller samples but risk bias with historical controls.
  • A novel Bayesian adaptive synthetic control design is proposed.

Purpose of the Study:

  • To present a new clinical trial design combining single-arm and RCT features.
  • To leverage historical control data efficiently.
  • To reduce sample size requirements and bias in experimental treatment evaluation.

Main Methods:

  • A two-stage Bayesian adaptive synthetic control design.
  • Stage 1: Single-arm enrollment with experimental treatment.
  • Stage 2: Evaluation of historical control data using propensity score matching and Bayesian prediction to form synthetic controls; switch to RCT if insufficient.
  • Performance assessed via computer simulation.

Main Results:

  • The Bayesian adaptive synthetic control design achieves power and unbiasedness comparable to RCTs.
  • It requires significantly smaller sample sizes when historical data is comparable.
  • Offers higher power and lower bias compared to traditional single-arm trials.

Conclusions:

  • The Bayesian adaptive synthetic control design enhances the efficiency of phase II clinical trials.
  • It effectively utilizes historical control data while mitigating bias.
  • Achieves RCT-level power with potentially reduced sample sizes.