# Covariate Adjustment in Basket Trials Borrowing Information Across Subgroups

## TL;DR

This paper introduces Bayesian hierarchical models that use ANCOVA to improve treatment effect estimation in basket trials by adjusting for covariates and borrowing information across subgroups.

## Contribution

The novelty lies in integrating ANCOVA with Bayesian hierarchical models for basket trials, exploring both with and without treatment-by-covariate interactions.

## Key findings

- Covariate-adjusted BHMs outperformed unadjusted BHMs and frequentist ANCOVA in simulation studies.
- The models were successfully applied to the MAJIC study, showing practical benefits in analyzing basket trials.
- Incorporating ANCOVA improved estimation precision in the context of patient heterogeneity and small sample sizes.

## Abstract

Basket trials are an efficient approach to simultaneously evaluate a single therapy across multiple diseases where patients share a common molecular target. Bayesian hierarchical models (BHMs) are widely used to estimate the treatment effects while accounting for heterogeneity between patient subgroups within a basket trial. However, the use of analysis of covariance (ANCOVA) with treatment‐by‐covariate interaction terms, in this context of patient heterogeneity and small samples, has been largely unexplored, despite the widespread use of ANCOVA for improving estimation precision in traditional settings from a frequentist perspective. In this paper, we propose two covariate‐adjusted BHMs that incorporate ANCOVA into the data model to enhance the estimation precision in basket trials, wherein borrowing of information is permitted across subgroups to a certain extent. Specifically, both ANCOVA without treatment‐by‐covariate interaction terms and ANCOVA with interaction terms are explored in the analysis of basket trials. We perform a simulation study to demonstrate the advantages of covariate‐adjusted BHMs compared to unadjusted BHMs, as well as frequentist ANCOVA models. The BHMs are then retrospectively applied to the analysis of the MAJIC study, a randomized controlled basket trial involving two subtypes of blood cancer.