Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods

Abstract Background In randomized clinical trials, treatment effects may vary, and this possibility is referred to as heterogeneity of treatment effect (HTE). One way to quantify HTE is to partition participants into subgroups based on individual’s risk of experiencing an outcome, then measuring tre...

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Bibliografiset tiedot

Päätekijät:	Edward Xu, Joseph Vanghelof, Yiyang Wang, Anisha Patel, Jacob Furst, Daniela Stan Raicu, Johannes Tobias Neumann, Rory Wolfe, Caroline X. Gao, John J. McNeil, Raj C. Shah, Roselyne Tchoua
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	BMC 2024-07-01
Sarja:	BMC Medical Research Methodology
Aiheet:	Heterogeneity of treatment effect Random forest Decision tree Aspirin Outcome risk modelling Disability-free longevity
Linkit:	https://doi.org/10.1186/s12874-024-02265-8
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