Assessing surrogate heterogeneity in real world data using meta-learners
Rebecca Knowlton, Layla Parast
TL;DR
This paper introduces a framework to assess surrogate marker heterogeneity in non-randomized data using machine learning methods.
Contribution
The novel framework allows evaluating surrogate heterogeneity in observational data while accounting for confounding.
Findings
The framework identifies covariate profiles where a surrogate is valid for the primary outcome.
Simulation studies and real-world application demonstrate the framework's effectiveness.
Hemoglobin A1c's surrogacy for fasting plasma glucose is examined for heterogeneity.
Abstract
Surrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes also extends to real-world public health and social science research, where randomized trials are often impractical. While standard methods for evaluating surrogate markers largely rely on the assumption of randomized treatment, there is a significant gap in applying these techniques to observational data, where the central challenge shifts to managing confounding. The few methods that do allow for non-randomized treatment/exposure do not offer a way to examine surrogate heterogeneity with respect to patient characteristics. In this paper, we propose a framework to assess surrogate heterogeneity in non-randomized data and implement this framework using meta-learners. Our approach allows us to quantify heterogeneity in surrogate strength with respect to…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Statistical Methods and Bayesian Inference · Statistical Methods in Clinical Trials