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PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. (Machine Learning)
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.ORCID iD: 0000-0002-5816-4345
Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.
Uppsala University, Akademiska Sjukhuset, Uppsala, Sweden.
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
John Wiley & Sons, 2019. Vol. 38, no 22, p. 4436-4452
National Category
Computer Sciences Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159305DOI: 10.1002/sim.8308ISI: 000484974200020PubMedID: 31246349Scopus ID: 2-s2.0-85068189287OAI: oai:DiVA.org:liu-159305DiVA, id: diva2:1340796
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-11-14Bibliographically approved

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Sysoev, OlegBartoszek, Krzysztof
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