Robust and Data-Adaptive Integration of Nonconcurrent Data in Platform Trials via Gaussian Processes

TL;DR

This paper introduces a Gaussian process-based method for safely incorporating nonconcurrent data into platform trial analyses, enhancing efficiency while controlling bias and uncertainty.

Contribution

It develops a data-adaptive Gaussian process framework that exploits temporal smoothness to integrate nonconcurrent data, with theoretical guarantees and practical implementation.

Findings

Incorporating nonconcurrent controls reduces posterior variance.

Bias from nonconcurrent data is bounded and controlled.

Framework extends to discrete outcomes and covariate adjustment.

Abstract

A platform trial is an innovative clinical trial design that enables simultaneous and continuous evaluation of multiple treatments within a single master protocol. Existing robust methods restrict analyses to concurrently randomized participants due to concerns that including nonconcurrent data may introduce bias from temporal trends. However, this exclusion represents a missed opportunity to improve efficiency. We propose a Gaussian process framework for incorporating nonconcurrent data that exploits temporal smoothness, a key feature of platform trials. The framework includes single-task and multi-task formulations and provides data-adaptive integration of nonconcurrent data with uncertainty quantification. The connection to kernel ridge regression yields a transparent frequentist interpretation of how nonconcurrent data are integrated. We establish two theoretical guarantees: incorporating nonconcurrent controls reduces the posterior variance of the treatment effect, and the resulting bias is controlled by a non-increasing bound. We extend the framework to discrete outcomes and to covariate adjustment, illustrate it on a hypothetical platform trial constructed from SURMOUNT-1, and provide an implementation in the R package RobinCID.