A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions
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DOI: 10.1007/s11634-013-0159-x
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- Marjolein Fokkema & Niels Smits & Achim Zeileis & Torsten Hothorn & Henk Kelderman, 2015. "Detecting Treatment-Subgroup Interactions in Clustered Data with Generalized Linear Mixed-Effects Model Trees," Working Papers 2015-10, Faculty of Economics and Statistics, Universität Innsbruck.
- Moritz Berger & Thomas Welchowski & Steffen Schmitz-Valckenberg & Matthias Schmid, 2019. "A classification tree approach for the modeling of competing risks in discrete time," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 965-990, December.
- Heidi Seibold & Torsten Hothorn & Achim Zeileis, 2019. "Generalised linear model trees with global additive effects," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 703-725, September.
- Veronica Distefano & Maria Mannone & Irene Poli, 2023. "Exploring Heterogeneity with Category and Cluster Analyses for Mixed Data," Stats, MDPI, vol. 6(3), pages 1-16, July.
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Keywords
Treatment heterogeneity; Recursive partitioning; Subgroup analysis; Treatment–subgroup interaction; 62H30; 62L99; 62P10; 62P15;All these keywords.
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