Doubly protected estimation for survival outcomes utilizing external controls for randomized clinical trials

TL;DR

This paper introduces a doubly protected estimator for survival analysis in clinical trials that effectively combines external control data with trial data, reducing bias and improving efficiency.

Contribution

It proposes a novel doubly protected estimation method that adjusts for covariate shifts and outcome drift, incorporating machine learning for flexible survival curve estimation.

Findings

The method reduces bias from external controls.

It improves efficiency over trial-only estimators.

Effective in real-world clinical trial data.

Abstract

Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical studies or real-world data to strengthen treatment evaluation for survival outcomes. However, using external controls directly may introduce biases due to data heterogeneity. We propose a doubly protected estimator for the treatment-specific restricted mean survival time difference that is more efficient than trial-only estimators and mitigates biases from external data. Our method adjusts for covariate shifts via doubly robust estimation and addresses outcome drift using the DR-Learner for selective borrowing. The approach can incorporate machine learning to approximate survival curves and detect outcome drifts without strict parametric assumptions, borrowing only comparable external controls. Extensive simulation studies and a real-data application evaluating the efficacy of Galcanezumab in mitigating migraine headaches have been conducted to illustrate the effectiveness of our proposed framework.