Statistical Methods to Evaluate Surrogate Markers

doi:10.1097/MLR.0000000000001956

Statistical Methods to Evaluate Surrogate Markers

2023-12-11

0

PRE

AI

DOI：10.1097/MLR.0000000000001956

Original

Original request for help

Save

Abstract

En 中文

Background:There is tremendous interest in evaluating surrogate markers given their potential to decrease study time, costs, and patient burden.Objectives:The purpose of this statistical workshop article is to describe and illustrate how to evaluate a surrogate marker of interest using the proportion of treatment effect (PTE) explained as a measure of the quality of the surrogate marker for: (1) a setting with a general fully observed primary outcome (eg, biopsy score); and (2) a setting with a time-to-event primary outcome which may be censored due to study termination or early drop out (eg, time to diabetes).Methods:The methods are motivated by 2 randomized trials, one among children with nonalcoholic fatty liver disease where the primary outcome was a change in biopsy score (general outcome) and another study among adults at high risk for Type 2 diabetes where the primary outcome was time to diabetes (time-to-event outcome). The methods are illustrated using the Rsurrogate package with a detailed R code provided.Results:In the biopsy score outcome setting, the estimated PTE of the examined surrogate marker was 0.182 (95% confidence interval [CI]: 0.121, 0.240), that is, the surrogate explained only 18.2% of the treatment effect on the biopsy score. In the diabetes setting, the estimated PTE of the surrogate marker was 0.596 (95% CI: 0.404, 0.760), that is, the surrogate explained 59.6% of the treatment effect on diabetes incidence.Conclusions:This statistical workshop provides tools that will support future researchers in the evaluation of surrogate markers.

Keywords:

surrogate marker

treatment effect

clinical trial

biostatistics

Statistical Methods to Evaluate Surrogate Markers

Abstract

Journal

Organization