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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 universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kvinnors och barns hälsa, Internationell mödra- och barnhälsovård (IMCH).
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kvinnors och barns hälsa, Internationell mödra- och barnhälsovård (IMCH).ORCID-id: 0000-0001-8036-168x
2019 (engelsk)Inngår i: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, nr 22, s. 4436-4452Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
2019. Vol. 38, nr 22, s. 4436-4452
Emneord [en]
bootstrap, decision trees, personalized medicine, random forest, subgroup discovery
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-395435DOI: 10.1002/sim.8308ISI: 000484974200020PubMedID: 31246349OAI: oai:DiVA.org:uu-395435DiVA, id: diva2:1366638
Forskningsfinansiär
Swedish Research Council, 2014-2161Tilgjengelig fra: 2019-10-30 Laget: 2019-10-30 Sist oppdatert: 2019-10-30bibliografisk kontrollert

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