Nonconcurrent Controls in Platform Trials: Can We Borrow Their Concurrent Observation Data?

Abstract

Adaptive platform trials (APTs) offer an innovative approach to studying multiple therapeutic interventions more efficiently through flexible features such as adding and dropping interventions as evidence emerges, creating a seamless process that avoids enrollment disruption. The benefits and practical challenges of implementing APTs have been widely discussed in the literature; however, less consideration has been given to how to use the nonconcurrent control (NCC) data (i.e., the data generated by patients recruited in the control arm before a new treatment is added) when the outcome of interest is a time to event endpoint. Including the NCC can increase the power of the trial. However, due to the omnipresent change of standard care over time, complete borrowing of the NCC survival data may lead to some bias in the estimation. In this article, we propose an alternative approach to borrow the concurrent observation part of the NCC data by left truncation using a simple decision-making flowchart, which can reduce the bias due to the change of standard care under certain assumptions. Then, the restricted mean survival time (RMST), estimated by the Kaplan-Meier method, is used to compare the treatment versus the pooled control group. We present two simulation studies to illustrate the performance of the decision-making flowchart method under different scenarios. We advocate researchers and drug developers to apply and validate this simple approach in practice.