Assurance Methods for designing a clinical trial with a delayed treatment effect

TL;DR

This paper introduces a Bayesian assurance calculation method tailored for clinical trials with delayed treatment effects, addressing uncertainty in key parameters to improve trial success rates, especially in immuno-oncology.

Contribution

It presents a novel elicitation technique and computational approach for assurance in trials with delayed effects, supported by open-source software.

Findings

Enhanced assurance calculation for delayed effects

Improved trial planning accuracy

Potential to increase success rates in immuno-oncology trials

Abstract

An assurance calculation is a Bayesian alternative to a power calculation. One may be performed to aid the planning of a clinical trial, specifically setting the sample size or to support decisions about whether or not to perform a study. Immuno-oncology is a rapidly evolving area in the development of anticancer drugs. A common phenomenon that arises in trials of such drugs is one of delayed treatment effects, that is, there is a delay in the separation of the survival curves. To calculate assurance for a trial in which a delayed treatment effect is likely to be present, uncertainty about key parameters needs to be considered. If uncertainty is not considered, the number of patients recruited may not be enough to ensure we have adequate statistical power to detect a clinically relevant treatment effect and the risk of an unsuccessful trial is increased. We present a new elicitation technique for when a delayed treatment effect is likely and show how to compute assurance using these elicited prior distributions. We provide an example to illustrate how this can be used in practice and develop open-source software to implement our methods. Our methodology has the potential to improve the success rate and efficiency of Phase III trials in immuno-oncology and for other treatments where a delayed treatment effect is expected to occur.