# Augmented binary method for basket trials (ABBA)

## TL;DR

This paper introduces a new statistical method for analyzing clinical trials that improves efficiency by combining data across different subtrials.

## Contribution

The ABBA method enhances power in basket trials by borrowing information across subtrials using a Bayesian hierarchical model.

## Key findings

- The proposed method reduces the 95% high-density interval of the posterior distribution of the log odds ratio.
- It increases statistical power when treatment effects are consistent across subtrials.
- The method is demonstrated using real data from clinical trials in rheumatology.

## Abstract

In several clinical areas, traditional clinical trials often use a responder outcome, a composite endpoint that involves dichotomising a continuous measure. An augmented binary method that improves power while retaining the original responder endpoint has previously been proposed. The method leverages information from the undichotomised component to improve power. We extend this method for basket trials, which are gaining popularity in many clinical areas. For clinical areas where response outcomes are used, we propose the new augmented binary method for basket trials that enhances efficiency by borrowing information on the treatment effect between subtrials. The method is developed within a latent variable framework using a Bayesian hierarchical modelling approach. We investigate the properties of the proposed methodology by analysing point estimates and high-density intervals in various simulation scenarios, comparing them to the standard analysis for basket trials that assumes binary outcomes. Our method results in a reduction of 95% high-density interval of the posterior distribution of the log odds ratio and an increase in power when the treatment effect is consistent across subtrials. We illustrate our approach using real data from two clinical trials in rheumatology.