Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

Journal: Pharmacoepidemiology and drug safety

Published Date: Jun 1, 2025

Abstract

PURPOSE: The surge of treatments for COVID-19 in the second quarter of 2020 had a low prevalence of treatment and high outcome risk. Motivated by that, we conducted a simulation study comparing disease risk scores (DRS) and propensity scores (PS) using a range of scenarios with different treatment prevalences and outcome risks.

Authors

Yuchen Guo

Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
Victoria Y Strauss

Boehringer Ingelheim, Binger Str. 173, 55218, Ingelheim am Rhein, Germany.
Sara Khalid

Center for Statistics in Medicine, Botnar Research Center, University of Oxford, Oxford, UK. Electronic address: sara.khalid@ndorms.ox.ac.uk.
Daniel Prieto-Alhambra

Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK.

Keywords

Bias Computer Simulation Confounding Factors, Epidemiologic COVID-19 COVID-19 Drug Treatment Humans Logistic Models Machine Learning Monte Carlo Method Prevalence Propensity Score Risk Assessment SARS-CoV-2

External Resources

View on PubMed Access via DOI PubMed (40457713)

Use of Machine Learning to Compare Disease Risk Scores and Propensity Scores Across Complex Confounding Scenarios: A Simulation Study.

Abstract

Authors

Keywords

External Resources

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