Improved Precision in the Analysis of Randomized Trials with Survival Outcomes, without Assuming Proportional Hazards

TL;DR

This paper introduces a new estimator for the restricted mean survival time in randomized trials that is robust to violations of proportional hazards and dependent censoring, offering improved precision and interpretability.

Contribution

The authors develop a novel estimator leveraging baseline variables, achieving better asymptotic precision and robustness compared to traditional methods like Kaplan-Meier, without assuming proportional hazards.

Findings

Estimator achieves 12% higher efficiency than Kaplan-Meier.

Remains consistent under dependent censoring when certain distributions are estimated.

Provides interpretability under violations of proportional hazards.

Abstract

We present a new estimator of the restricted mean survival time in randomized trials where there is right censoring that may depend on treatment and baseline variables. The proposed estimator leverages prognostic baseline variables to obtain equal or better asymptotic precision compared to traditional estimators. Under regularity conditions and random censoring within strata of treatment and baseline variables, the proposed estimator has the following features: (i) it is interpretable under violations of the proportional hazards assumption; (ii) it is consistent and at least as precise as the Kaplan-Meier estimator under independent censoring; (iii) it remains consistent under violations of independent censoring (unlike the Kaplan-Meier estimator) when either the censoring or survival distributions are estimated consistently; and (iv) it achieves the nonparametric efficiency bound when both of these distributions are consistently estimated. We illustrate the performance of our method using simulations based on resampling data from a completed, phase 3 randomized clinical trial of a new surgical treatment for stroke; the proposed estimator achieves a 12% gain in relative efficiency compared to the Kaplan-Meier estimator. The proposed estimator has potential advantages over existing approaches for randomized trials with time-to-event outcomes, since existing methods either rely on model assumptions that are untenable in many applications, or lack some of the efficiency and consistency properties (i)-(iv). We focus on estimation of the restricted mean survival time, but our methods may be adapted to estimate any treatment effect measure defined as a smooth contrast between the survival curves for each study arm. We provide R code to implement the estimator.