Bayesian Design and Analysis of Precision Trials with Partial Borrowing

TL;DR

An individually weighted model where the external data are weighted based on their fit with the target population based on the distribution of a set of covariates is proposed and a Bayesian design framework where design priors are extracted from the external data to determine decision boundaries and sample sizes is provided.

Abstract

With the advancement of precision medicine there is an increasing need for design and analysis methods in clinical trials with the objective of investigating effect heterogeneity and estimating subgroup effects. As this requires precise estimation of interaction effects, borrowing information from external data sources including retrospective studies and early phase clinical trials to enrich the trial in sparse subgroups is pertinent. Motivated by a trial in gastric cancer we consider a practical design and analysis framework for borrowing from external data sources that only partially inform the inference. As the analysis model we propose an individually weighted model where the external data are weighted based on their fit with the target population based on the distribution of a set of covariates. In a simulation study we assess the performance of the model under various scenarios and make comparisons to dynamic borrowing. In addition, we provide a Bayesian design framework where design priors are extracted from the external data to determine decision boundaries and sample sizes. The design procedure is demonstrated within the context of our motivating example.

Figures (5)

• Figure 1: Deviation of posterior medians from the true marginal subgroup effect across 100 Monte Carlo iterations
• Figure 2: Posterior root mean squared error fore the marginal subgroup effect across 100 Monte Carlo iterations
• Figure 3: The posterior distributions of the marginalized treatment effect among patients with recurrent disease status based on only the XParTS-II trial data (bottom), partial borrowing (middle) and full borrowing (top).
• Figure 4: The posterior distributions of the marginalized treatment effect given the external data used to define the null and alternative design priors
• Figure 5: Bayesian power curves with and without borrowing. The dotted vertical lines indicate the sample size required to achieve 60%, 70% and 80% power.