Design of phase III trials with long-term survival outcomes based on short-term binary results

TL;DR

This paper proposes a statistical framework for designing phase III cancer trials using short-term binary response data to predict long-term survival outcomes, facilitating more efficient trial planning.

Contribution

It introduces a mixture model linking binary response and survival data, providing sample size calculations and an R package for trial design based on prior pCR information.

Findings

The proposed method accurately predicts survival outcomes from binary responses.

Sample size calculations are adaptable to various scenarios.

Simulation studies validate the effectiveness of the approach.

Abstract

Pathologic complete response (pCR) is a common primary endpoint for a phase II trial or even accelerated approval of neoadjuvant cancer therapy. If granted, a two-arm confirmatory trial is often required to demonstrate the efficacy with a time-to-event outcome such as overall survival. However, the design of a subsequent phase III trial based on prior information on the pCR effect is not straightforward. Aiming at designing such phase III trials with overall survival as primary endpoint using pCR information from previous trials, we consider a mixture model that incorporates both the survival and the binary endpoints. We propose to base the comparison between arms on the difference of the restricted mean survival times, and show how the effect size and sample size for overall survival rely on the probability of the binary response and the survival distribution by response status, both for each treatment arm. Moreover, we provide the sample size calculation under different scenarios and accompany them with an R package where all the computations have been implemented. We evaluate our proposal with a simulation study, and illustrate its application through a neoadjuvant breast cancer trial.