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 crucial 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 approach 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.
CITATION STYLE
Tazare, J., Smeeth, L., Evans, S. J. W., Douglas, I. J., & Williamson, E. J. (2023). hdps: A suite of commands for applying high-dimensional propensity-score approaches. Stata Journal, 23(3), 683–708. https://doi.org/10.1177/1536867X231196288
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