Robust Evaluation of Longitudinal Surrogate Markers with Censored Data

TL;DR

This paper introduces robust statistical methods to evaluate the effectiveness of longitudinal surrogate markers in censored time-to-event clinical data, addressing a gap in existing methods.

Contribution

It develops new techniques to quantify the treatment effect explained by longitudinal surrogate markers in censored survival data, accommodating measurement and outcome censoring.

Findings

Methods show good finite-sample performance in simulations.

Application to Diabetes Prevention Program data illustrates practical utility.

Provides a framework for surrogate marker evaluation in complex clinical settings.

Abstract

The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g., fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the methods have not been developed for use with primary outcomes that are time-to-event outcomes and/or subject to censoring. In this paper, we propose robust methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting. Specifically, we propose a method to define and estimate the proportion of the treatment effect on a censored primary outcome that is explained by the treatment effect on a longitudinal surrogate marker measured up to time $t_0$. We accommodate both potential censoring of the primary outcome and of the surrogate marker. A simulation study demonstrates good finite-sample performance of our proposed methods. We illustrate our procedures by examining repeated measures of fasting plasma glucose, a surrogate marker for diabetes diagnosis, using data from the Diabetes Prevention Program (DPP).