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A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions

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  • L. Doove
  • E. Dusseldorp
  • K. Deun
  • I. Mechelen

Abstract

In case multiple treatment alternatives are available for some medical problem, the detection of treatment–subgroup interactions (i.e., relative treatment effectiveness varying over subgroups of persons) is of key importance for personalized medicine and the development of optimal treatment assignment strategies. Randomized Clinical Trials (RCT) often go without clear a priori hypotheses on the subgroups involved in treatment–subgroup interactions, and with a large number of pre-treatment characteristics in the data. In such situations, relevant subgroups (defined in terms of pre-treatment characteristics) are to be induced during the actual data analysis. This comes down to a problem of cluster analysis, with the goal of this analysis being to find clusters of persons that are involved in meaningful treatment–person cluster interactions. For such a cluster analysis, five recently proposed methods can be used, all being of a recursive partitioning type. However, these five methods have been developed almost independently, and the relations between them are not yet understood. The present paper closes this gap. It starts by outlining the basic principles behind each method, and by illustrating it with an application on an RCT data set on two treatment strategies for substance abuse problems. Next, it presents a comparison of the methods, hereby focusing on major similarities and differences. The discussion concludes with practical advice for end users with regard to the selection of a suitable method, and with an important challenge for future research in this area. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • L. Doove & E. Dusseldorp & K. Deun & I. Mechelen, 2014. "A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions," 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. 8(4), pages 403-425, December.
    • Handle: RePEc:spr:advdac:v:8:y:2014:i:4:p:403-425
    DOI: 10.1007/s11634-013-0159-x
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    References listed on IDEAS

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    1. Dehejia, Rajeev H., 2005. "Program evaluation as a decision problem," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 141-173.
    2. Elise Dusseldorp & Jacqueline Meulman, 2004. "The regression trunk approach to discover treatment covariate interaction," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 355-374, September.
    3. Juliet Shaffer, 1991. "Probability of directional errors with disordinal (qualitative) interaction," Psychometrika, Springer;The Psychometric Society, vol. 56(1), pages 29-38, March.
    4. Su Xiaogang & Zhou Tianni & Yan Xin & Fan Juanjuan & Yang Song, 2008. "Interaction Trees with Censored Survival Data," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-28, January.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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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