Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis

Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis

IntroductionMachine learning (ML) methods are promising and scalable alternatives for propensity score (PS) estimation, but their comparative performance in disease risk score (DRS) estimation remains unexplored.MethodsWe used real-world data comparing antihypertensive users to non-users with 69 neg...

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Bibliographic Details
Main Authors: Yuchen Guo, Victoria Y. Strauss, Martí Català, Annika M. Jödicke, Sara Khalid, Daniel Prieto-Alhambra
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-10-01
Series:Frontiers in Pharmacology
Subjects:
treatment effect
observational research
machine learning
propensity scores
disease risk scores
negative control
Online Access:https://www.frontiersin.org/articles/10.3389/fphar.2024.1395707/full

Internet

https://www.frontiersin.org/articles/10.3389/fphar.2024.1395707/full

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