A Bayesian Critique of Rank-Based Methods for Surrogate Marker Evaluation

TL;DR

This paper introduces a Bayesian method for surrogate marker evaluation that improves causal interpretability and statistical power over existing rank-based approaches, demonstrated through simulation results.

Contribution

The paper proposes a Bayesian framework for surrogate marker evaluation that addresses limitations of existing rank-based methods, enhancing causal inference and statistical power.

Findings

Bayesian approach improves causal interpretation of surrogate markers.

Enhanced statistical power through covariate adjustment.

Simulation study shows increased accuracy and power.

Abstract

Surrogate markers are often employed in clinical trials to replace primary outcomes that may be difficult, expensive, or time-consuming to measure directly. These markers can accelerate the evaluation of new treatments, provided they reliably capture the causal relationship between treatment and true clinical benefit. Parast et al. (2024) recently proposed a rank-based approach for evaluating surrogate markers, characterized by its nonparametric nature and minimal assumptions. While this method is useful in small-sample model-agnostic settings, it has several limitations, including a lack of clear causal interpretation, low statistical power, and insufficient robustness to different data-generating mechanisms. In this paper, we propose a Bayesian approach that addresses these shortcomings by focusing on causal treatment effect estimands and, in doing so, improves power through covariate adjustment. We demonstrate the advantages of our proposed method through a simulation study designed to highlight gains in both accuracy and power.