2025/04/21 by Rebecca Kathryn Knowlton, Layla Parast, Knowlton, Rebecca +1
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2504.15386
openalex publication_date 2025/04/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Surrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes extends beyond these settings and may be more pronounced in real-world public health and social science research, where randomized trials are often impractical. Research on identifying surrogates in real-world non-randomized data is scarce, as available statistical approaches for evaluating surrogate markers tend to rely on the assumption that treatment is randomized. While the few methods that allow for non-randomized treatment/exposure appropriately handle confounding individual characteristics, they 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 real-world, i.e., non-randomized, data and implement this framework using various 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 individuals for whom 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.