Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials

TL;DR

This paper reviews Bayesian models and decision algorithms tailored for complex early phase clinical trials, highlighting their design, application, and potential benefits despite practical challenges.

Contribution

It introduces and discusses Bayesian early phase trial designs adapted for complex clinical settings, emphasizing their advantages and implementation considerations.

Findings

Bayesian designs improve dose-finding accuracy.

Outcome-adaptive rules enhance patient safety.

Despite complexity, Bayesian methods show promising clinical benefits.

Abstract

An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community's resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.