A cautious use of auxiliary outcomes for decision-making in randomized clinical trials

TL;DR

This paper introduces a Bayesian decision-theoretic framework for using both primary and auxiliary outcomes in clinical trial decision-making, improving efficiency without compromising error control.

Contribution

It develops a novel approach that incorporates auxiliary outcomes into trial decisions, controlling error rates without assuming outcome concordance.

Findings

Framework controls type I error rate rigorously.

Incorporating auxiliary outcomes yields efficiency gains.

Applicable to emerging technologies like circulating tumor assays.

Abstract

Clinical trials often collect data on multiple outcomes, such as overall survival (OS), progression-free survival (PFS), and response to treatment (RT). In most cases, however, study designs only use primary outcome data for interim and final decision-making. In several disease settings, clinically relevant outcomes, for example OS, become available years after patient enrollment. Moreover, the effects of experimental treatments on OS might be less pronounced compared to auxiliary outcomes such as RT. We develop a Bayesian decision-theoretic framework that uses both primary and auxiliary outcomes for interim and final decision-making. The framework allows investigators to control standard frequentist operating characteristics, such as the type I error rate and can be used with auxiliary outcomes from emerging technologies, such as circulating tumor assays. False positive rates and other frequentist operating characteristics are rigorously controlled without any assumption about the concordance between primary and auxiliary outcomes. We discuss algorithms to implement this decision-theoretic approach and show that incorporating auxiliary information into interim and final decision-making can lead to relevant efficiency gains according to established and interpretable metrics.