hdps: A suite of commands for applying high-dimensional propensity-score approaches

John Tazare (), Liam Smeeth (), Stephen J. W. Evans (), Ian J. Douglas () and Elizabeth J. Williamson ()
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John Tazare: London School of Hygiene and Tropical Medicine
Liam Smeeth: London School of Hygiene and Tropical Medicine
Stephen J. W. Evans: London School of Hygiene and Tropical Medicine
Ian J. Douglas: London School of Hygiene and Tropical Medicine
Elizabeth J. Williamson: London School of Hygiene and Tropical Medicine

Stata Journal, 2023, vol. 23, issue 3, 683-708

Abstract: Large healthcare databases are increasingly used for research investigating the effects of medications. However, a key challenge is capturing hard-to-measure concepts (often relating to frailty and disease severity) that can be cru- cial for successful confounder adjustment. The high-dimensional propensity score has been proposed as a data-driven method to improve confounder adjustment within healthcare databases and was developed in the context of administrative claims databases. We present hdps, a suite of commands implementing this ap- proach in Stata that assesses the prevalence of codes, generates high-dimensional propensity-score covariates, performs variable selection, and provides investigators with graphical tools for inspecting the properties of selected covariates.

Keywords: hdps setup; hdps prevalence; hdps recurrence; hdps prioritize; hdps graphics; electronic health records; claims databases; propensity score; confounder adjustment (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1177/1536867X231196288

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