Incorporating Surrogate Information for Adaptive Subgroup Enrichment Design with Sample Size Re-estimation

TL;DR

This paper enhances adaptive subgroup enrichment trial designs by integrating surrogate endpoint information to improve interim decision accuracy, sample size re-estimation, and overall trial power, especially with limited early data.

Contribution

It introduces a modified conditional power method that incorporates surrogate endpoint data for better interim decisions and sample size adjustments in adaptive trials.

Findings

Higher probability of desirable interim decisions

Increased overall trial power

Robust performance despite prior knowledge drift

Abstract

Adaptive subgroup enrichment design is an efficient design framework that allows accelerated development for investigational treatments while also having flexibility in population selection within the course of the trial. The adaptive decision at the interim analysis is commonly made based on the conditional probability of trial success. However, one of the critical challenges for such adaptive designs is immature data for interim decisions, particularly in the targeted subgroup with a limited sample size at the first stage of the trial. In this paper, we improve the interim decision making by incorporating information from surrogate endpoints when estimating conditional power at the interim analysis, by predicting the primary treatment effect based on the observed surrogate endpoint and prior knowledge or historical data about the relationship between endpoints. Modified conditional power is developed for both selecting the patient population to be enrolled after the interim analysis and sample size re-estimation. In the simulation study, our proposed design shows a higher chance to make desirable interim decisions and achieves higher overall power, while controlling the overall type I error. This performance is robust over drift of prior knowledge from the true relationship between two endpoints. We also demonstrate the application of our proposed design in two case studies in oncology and vaccine trials.