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Causal rule ensemble method for estimating heterogeneous treatment effect with consideration of prognostic effects.
Hiraishi, Mayu; Wan, Ke; Tanioka, Kensuke; Yadohisa, Hiroshi; Shimokawa, Toshio.
Afiliação
  • Hiraishi M; Clinical Study Support Center, Wakayama Medical University Hospital, Wakayama, Japan.
  • Wan K; Graduate School of Culture and Information Science, Doshisha University, Kyoto, Japan.
  • Tanioka K; Department of Medical Data Science, Graduate School of Medicine, Wakayama Medical University, Wakayama, Japan.
  • Yadohisa H; Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan.
  • Shimokawa T; Department of Culture and Information Science, Doshisha University, Kyoto, Japan.
Stat Methods Med Res ; 33(6): 1021-1042, 2024 Jun.
Article em En | MEDLINE | ID: mdl-38676367
ABSTRACT
We propose a novel framework based on the RuleFit method to estimate heterogeneous treatment effect in randomized clinical trials. The proposed method estimates a rule ensemble comprising a set of prognostic rules, a set of prescriptive rules, as well as the linear effects of the original predictor variables. The prescriptive rules provide an interpretable description of the heterogeneous treatment effect. By including a prognostic term in the proposed model, the selected rule is represented as an heterogeneous treatment effect that excludes other effects. We confirmed that the performance of the proposed method was equivalent to that of other ensemble learning methods through numerical simulations and demonstrated the interpretation of the proposed method using a real data application.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ensaios Clínicos Controlados Aleatórios como Assunto / Modelos Estatísticos Limite: Humans Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Japão

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Ensaios Clínicos Controlados Aleatórios como Assunto / Modelos Estatísticos Limite: Humans Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Japão