# An efficient single-arm Bayesian adaptive trial algorithm to evaluate de-intensified oncologic treatment

## TL;DR

This paper presents a Bayesian adaptive trial method to efficiently evaluate less intensive cancer treatments, especially in rare cancers, using prior data and interim analyses.

## Contribution

A novel Bayesian adaptive algorithm for single-arm trials that integrates historical data and supports multi-stage analyses for de-intensified oncologic treatments.

## Key findings

- The method enables robust estimation and decision-making with limited event data in rare cancers.
- The R package 'BayesAT' provides flexible modeling and supports multi-stage interim analyses.
- The algorithm was successfully applied to a pediatric Hodgkin lymphoma trial, demonstrating its practical utility.

## Abstract

In clinical trials, evaluating de-intensified oncologic treatment strategies can help reduce treatment-related toxicities while preserving patients’ quality of life. However, de-intensification is typically evaluated in cancers with a low relapse rate, and if the cancer type is uncommon, a randomized trial may require an impractically extended period to accumulate sufficient events for reliable inferential conclusions.

This paper introduces a Bayesian adaptive method for the single-arm trial design that provides efficient analysis of survival data under these constraints. By incorporating data from previous studies to establish prior knowledge and a historical control arm, this approach enables robust and accurate estimations and predictions for trial design, sample size determination, and inferential decision-making. To support the implementation of this method, we developed an R package called “BayesAT,” which offers significant flexibility in modelling and supports multi-stage interim analyses, particularly for evaluating de-intensified oncologic treatments.

Our approach is validated through comprehensive simulation studies and sensitivity analyses. Additionally, this algorithm has been applied to a pediatric Hodgkin lymphoma trial, showcasing its capability to effectively leverage information from previous studies and conduct interim analyses that expedite conclusions regarding treatment efficacy.