Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions

Abstract

We propose and analyze the first model for clinical trial design that integrates each of three important trends intending to improve the effectiveness of clinical trials that inform health-technology adoption decisions: adaptive design, which dynamically adjusts the sample size and allocation of interventions to different patients; multi-arm trial design, which compares multiple interventions simultaneously; and value-based design, which focuses on cost-beneft improvements of health interventions over a current standard of care. Example applications are to seamless Phase II/III dose-finding trials and to trials that test multiple combinations of therapies. Our objective is to maximize the expected population health-economic benefit of health-technology adoption decisions less clinical trial costs. We show that unifying the adaptive, multi-arm, and value-based approaches to trial design can reduce the cost and duration of multi-arm trials with efficient adaptive lookahead policies that focus on value to patients, and account for correlated rewards across arms. Features that differentiate our approach from much other work on stochastic optimization include stopping times that balance sampling costs and the expected value of information of those samples, performance guarantees offered by new asymptotic convergence proofs, and the modeling of arms' potentially different sampling costs. Our proposed solution can be computed feasibly and can randomize patients. The class of trials for the base model assumes that health-economic data are collected and observed quickly. Related work from Bayesian optimization can enable the further inclusion of trials with intermediate duration delays between the time of treatment initiation and observation of outcomes.