Correcting for Missing Data When Evaluating Surrogate Markers in a Clinical Trial

2026-03-21

AI Summary

Existing approaches to surrogate marker evaluation often ignore missing data and rely on complete-case analyses, which can introduce bias and reduce statistical efficiency. This work proposes a unified framework that, for the first time, systematically incorporates missing data correction into surrogate marker validation by integrating inverse probability weighting (IPW) with semiparametric maximum likelihood estimation (SMLE). The framework ensures robustness, computational tractability, and high statistical efficiency under both nonparametric and parametric settings. An accompanying R package, MissSurrogate, implements the proposed methodology. Simulation studies demonstrate that the method remains unbiased and achieves near-full-sample efficiency across various missing data mechanisms. Its practical utility is further illustrated through an application to a diabetes clinical trial.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Evaluating treatment effects is critical in clinical trials but sometimes involves lengthy, invasive, or costly follow-up procedures. In these cases, surrogate markers, which provide intermediate measures of the long-term treatment effect, allow clinicians to obtain results faster and more efficiently than would have otherwise been possible. Prior to adoption, it is vital that the utility of surrogate markers (i.e., their ability to capture the treatment effect on the primary outcome) is statistically validated. Many frameworks for evaluating surrogate markers have been proposed, but they do not account for missing data. Instead, they rely on complete cases (the subset of patients without missing data), which can be inefficient and biased. To improve on this, we propose methods to accommodate missing data in nonparametric and parametric surrogate evaluation via inverse probability weighting (IPW) and semiparametric maximum likelihood estimation (SMLE). Through simulation studies, we demonstrate that the proposed methods remain unbiased under a broader range of missing data mechanisms than complete case analysis and can help retain the statistical precision of the full trial. We illustrate their practical utility through an application to a diabetes clinical trial. Moreover, our missing data corrections have complementary strengths with respect to computational ease, robustness, and statistical efficiency. All methods are implemented in the MissSurrogate R package.

Problem

• surrogate markers
• missing data
• clinical trials
• treatment effect
• statistical validation

Innovation

• surrogate markers
• missing data
• inverse probability weighting
• semiparametric maximum likelihood estimation
• clinical trials