A Bayesian response-adaptive dose finding and comparative effectiveness trial

TL;DR

This paper introduces a Bayesian adaptive trial design combining dose finding and comparative effectiveness analysis, improving efficiency and accuracy in evaluating combination therapies with fewer participants.

Contribution

It develops a novel Bayesian response-adaptive trial framework that integrates dose optimization and effectiveness comparison, demonstrated via simulation in pediatric pain management.

Findings

83% chance of allocating most patients to the best dose

Type I error less than 5% for effectiveness comparison

Over 90% Bayesian predictive power

Abstract

Aims: Combinations of treatments can offer additional benefit over the treatments individually. However, trials of these combinations are lower priority than the development of novel therapies, which can restrict funding, timelines and patient availability. This paper develops a novel trial design to facilitate the evaluation of novel combination therapies that combines elements of phase II and phase III trials. Methods: This trial uses response adaptive randomisation to increase the information collected about successful novel drug combinations and Bayesian dose-response modelling to undertake a comparative-effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods to evaluate the probability of selecting the correct optimal dose combination, the operating characteristics and predictive power of this design for a trial in pain management and sedation in paediatric emergency departments. Results: With 410 participants, 5 interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correctly concluding non-inferiority when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a 77% chance of meeting its dual aims of dose finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. Conclusion: The proposed trial has high potential to meet the dual study objectives within a feasible level of recruitment, minimising the administrative burden and recruitment time for a trial.