Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision

TL;DR

This paper introduces a covariate adjusted pseudovalue regression method for estimating differences in restricted mean survival times, enhancing precision and enabling smaller, more efficient clinical trials without bias.

Contribution

The paper proposes a novel covariate adjustment technique for RMST estimation using pseudovalue regression, improving precision while maintaining error control.

Findings

Increased precision in treatment effect estimation.

Ability to quantify precision gains for sample size planning.

Maintains strict type I error control without bias.

Abstract

Covariate adjustment is desired by both practitioners and regulators of randomized clinical trials because it improves precision for estimating treatment effects. However, covariate adjustment presents a particular challenge in time-to-event analysis. We propose to apply covariate adjusted pseudovalue regression to estimate between-treatment difference in restricted mean survival times (RMST). Our proposed method incorporates a prognostic covariate to increase precision of treatment effect estimate, maintaining strict type I error control without introducing bias. In addition, the amount of increase in precision can be quantified and taken into account in sample size calculation at the study design stage. Consequently, our proposed method provides the ability to design smaller randomized studies at no expense to statistical power.