The Target Study: A Conceptual Model and Framework for Measuring Disparity

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.

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Publication Details

Journal
Sociological Methods & Research
Published
2025-04-22
DOI
https://doi.org/10.1177/00491241251314037
Citations
4
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
10.52

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article

The Target Study: A Conceptual Model and Framework for Measuring Disparity

Raquel C. Greer, Chanelle J. Howe, John Wyse Jackson, Yea‐Jen Hsu et al.
4 citations
Sociological Methods & Research
Advanced Causal Inference Techniques
10.52
article

The Target Study: A Conceptual Model and Framework for Measuring Disparity

Raquel C. Greer, Chanelle J. Howe, John Wyse Jackson, Yea‐Jen Hsu, Romsai T. Boonyasai
article en
4 citations

Abstract

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.

Sociological Methods & Research
Johns Hopkins University (US), John Brown University (US), Brown University (US), Johns Hopkins Medicine (US), Office of Extramural Research (US), Agency for Healthcare Research and Quality (US)
National Institutes of Health
Openalex Percentile: Top 2%
Advanced Causal Inference Techniques
10.52
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