BASIC: A Bayesian Adaptive Synthetic-Control Design for Phase II Clinical Trials

Authors

Liyun Jiang
Peter F Thall
Fangrong Yan
Scott Kopetz
Ying Yuan

Publication Date

10-1-2023

Journal

Clinical Trals

Abstract

Background: Randomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial.

Methods: The Bayesian adaptive synthetic control design has two stages. In stage 1, a prespecified number of patients are enrolled in a single arm given the experimental treatment. Based on the stage 1 data, applying propensity score matching and Bayesian posterior prediction methods, the usefulness of the historical control data for identifying a pseudo sample of matched synthetic-control patients for making comparative inferences is evaluated. If a sufficient number of synthetic controls can be identified, the single-arm trial is continued. If not, the trial is switched to a randomized controlled trial. The performance of The Bayesian adaptive synthetic control design is evaluated by computer simulation.

Results: The Bayesian adaptive synthetic control design achieves power and unbiasedness similar to a randomized controlled trial but on average requires a much smaller sample size, provided that the historical control data patients are sufficiently comparable to the trial patients so that a good number of matched controls can be identified in the historical control data. Compared to a single-arm trial, The Bayesian adaptive synthetic control design yields much higher power and much smaller bias.

Conclusion: The Bayesian adaptive synthetic-control design provides a useful tool for exploiting historical control data to improve the efficiency of single-arm phase II clinical trials, while addressing the problem of bias when comparing trial results to historical control data. The proposed design achieves power similar to a randomized controlled trial but may require a substantially smaller sample size.

Keywords

Humans, Bayes Theorem, Bias, Computer Simulation, Randomized Controlled Trials as Topic, Research Design, Sample Size, Clinical Trials, Phase II as Topic, Bayesian adaptive design, Real-world data, augmented control, historical data, randomized controlled trials

DOI

10.1177/17407745231176445

PMID

37313712

PMCID

PMC10504821

PubMedCentral® Posted Date

6-14-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes