2022/07/01 by John W. Jackson, Yea‐Jen Hsu, Jackson, John W. +7
Health Professions · Mathematics · Social Sciences · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Health disparities and outcomes #Methodology (stat.ME) #Primary Care and Health Outcomes
paper · pdf · doi:10.48550/arxiv.2207.00530
openalex publication_date 2022/07/01 · openalex created_date 2022/07/06 · openalex updated_date 2026/07/28
We present a conceptual model to measure disparity--the target study--where social groups may be similarly situated (i.e., balanced) on allowable covariates. Our model, based on a sampling design, does not intervene to assign social group membership or alter allowable covariates. To address non-random sample selection, we extend our model to generalize or transport disparity or to assess disparity after an intervention on eligibility-related variables that eliminates forms of collider-stratification. To avoid bias from differential timing of enrollment, we aggregate time-specific study results by balancing calendar time of enrollment across social groups. To provide a framework for emulating our model, we discuss study designs, data structures, and G-computation and weighting estimators. We compare our sampling-based model to prominent decomposition-based models used in healthcare and algorithmic fairness. We provide R code for all estimators and apply our methods to measure health system disparities in hypertension control using electronic medical records.