Evaluating Surrogate Marker Information using Censored Data

TL;DR

This paper introduces a new nonparametric method to evaluate surrogate markers in time-to-event studies with censored data, addressing challenges of prior methods and enabling more accurate surrogate validation.

Contribution

It proposes a novel definition and robust estimation procedure for surrogate marker evaluation in censored time-to-event data, accommodating pre-measurement of surrogates and censoring issues.

Findings

Method performs well in simulations

Applied to diabetes data for potential surrogate markers

Provides a practical tool for surrogate validation in clinical trials

Abstract

Given the long follow-up periods that are often required for treatment or intervention studies, the potential to use surrogate markers to decrease the required follow-up time is a very attractive goal. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Currently available methods for identifying and validating surrogate markers tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. The ability to use such methods in practice when the primary outcome of interest is a time-to-event outcome is difficult due to censoring and missing surrogate information among those who experience the primary outcome before surrogate marker measurement. In this paper, we propose a novel definition of the proportion of treatment effect explained by surrogate information collected up to a specified time in the setting of a time-to-event primary outcome. Our proposed approach accommodates a setting where individuals may experience the primary outcome before the surrogate marker is measured. We propose a robust nonparametric procedure to estimate the defined quantity using censored data and use a perturbation-resampling procedure for variance estimation. Simulation studies demonstrate that the proposed procedures perform well in finite samples. We illustrate the proposed procedures by investigating two potential surrogate markers for diabetes using data from the Diabetes Prevention Program.