How to Add Baskets to an Ongoing Basket Trial with Information Borrowing

TL;DR

This paper investigates methods for adding new baskets to ongoing basket trials using Bayesian information borrowing, proposing a calibration approach to improve decision robustness and analyzing the impact on trial power and error rates.

Contribution

It introduces a novel calibration method for decision criteria in basket trials and evaluates strategies for incorporating new baskets into ongoing studies with Bayesian borrowing.

Findings

Adding baskets with information borrowing improves power

Calibration reduces false positive rates

Borrowing can inflate error rates without proper calibration

Abstract

Basket trials test a single therapeutic treatment on several patient populations under one master protocol. A desirable adaptive design feature in these studies may be the incorporation of new baskets to an ongoing study. Limited basket sample sizes can cause issues in power and precision of treatment effect estimates which could be amplified in added baskets due to the shortened recruitment time. While various Bayesian information borrowing techniques have been introduced to tackle the issue of small sample sizes, the impact of including new baskets in the trial and into the borrowing model has yet to be investigated. We explore approaches for adding baskets to an ongoing trial under information borrowing and highlight when it is beneficial to add a basket compared to running a separate investigation for new baskets. We also propose a novel calibration approach for the decision criteria that is more robust to false decision making. Simulation studies are conducted to assess the performance of approaches which is monitored primarily through type I error control and precision of estimates. Results display a substantial improvement in power for a new basket when information borrowing is utilized, however, this comes with potential inflation of error rates which can be shown to be reduced under the proposed calibration procedure.