Adaptive enrichment trial designs using joint modeling of longitudinal and time-to-event data

TL;DR

This paper introduces a joint modeling approach for longitudinal biomarkers and time-to-event data to enhance adaptive enrichment trial designs, improving subgroup identification, efficiency, and interim decision-making.

Contribution

It presents a novel joint model and methods for standardizing statistics to support adaptive enrichment and early stopping in clinical trials.

Findings

Joint model effectively integrates biomarker and survival data.

Incorporating biomarkers improves subgroup detection accuracy.

Simulation shows increased trial power with the proposed method.

Abstract

Adaptive enrichment allows for pre-defined patient subgroups of interest to be investigated throughout the course of a clinical trial. Many trials which measure a long-term time-to-event endpoint often also routinely collect repeated measures on biomarkers which may be predictive of the primary endpoint. Although these data may not be leveraged directly to support subgroup selection decisions and early stopping decisions, we aim to make greater use of these data to increase efficiency and improve interim decision making. In this work, we present a joint model for longitudinal and time-to-event data and two methods for creating standardised statistics based on this joint model. We can use the estimates to define enrichment rules and efficacy and futility early stopping rules for a flexible efficient clinical trial with possible enrichment. Under this framework, we show asymptotically that the familywise error rate is protected in the strong sense. To assess the results, we consider a trial for the treatment of metastatic breast cancer where repeated ctDNA measurements are available and the subgroup criteria is defined by patients' ER and HER2 status. Using simulation, we show that incorporating biomarker information leads to accurate subgroup identification and increases in power.