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PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
Linköping Univ, Dept Comp & Informat Sci, Linköping, Sweden.ORCID iD: 0000-0002-3092-4162
Linköping Univ, Dept Comp & Informat Sci, Linköping, Sweden.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH).
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, International Maternal and Child Health (IMCH).ORCID iD: 0000-0001-8036-168x
2019 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 22, p. 4436-4452Article in journal (Refereed) Published
Abstract [en]

Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two-arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community-based nutrition intervention trial that justifies the validity of our method.

Place, publisher, year, edition, pages
2019. Vol. 38, no 22, p. 4436-4452
Keywords [en]
bootstrap, decision trees, personalized medicine, random forest, subgroup discovery
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-395435DOI: 10.1002/sim.8308ISI: 000484974200020PubMedID: 31246349OAI: oai:DiVA.org:uu-395435DiVA, id: diva2:1366638
Funder
Swedish Research Council, 2014-2161Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-10-30Bibliographically approved

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Sysoev, OlegEkström, Eva-CharlotteEkholm Selling, Katarina
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