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 patient characteristics while accommodating confounders through the use of flexible, off-the-shelf machine learning methods. In addition, we use our framework to identify covariate profiles where the surrogate is a valid replacement of the primary outcome. We examine the performance of our methods via a simulation study and application to examine heterogeneity in the surrogacy of hemoglobin A1c as a surrogate for fasting plasma glucose.
Knowlton et al. (Thu,) studied this question.