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

Author

Listed:
  • 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)

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.

Suggested Citation

  • John Tazare & Liam Smeeth & Stephen J. W. Evans & Ian J. Douglas & Elizabeth J. Williamson, 2023. "hdps: A suite of commands for applying high-dimensional propensity-score approaches," Stata Journal, StataCorp LP, vol. 23(3), pages 683-708, September.
    • Handle: RePEc:tsj:stataj:v:23:y:2023:i:3:p:683-708
    DOI: 10.1177/1536867X231196288
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