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PSICA: Decision trees for probabilistic subgroup identification with categorical treatments

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 identificatio...

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Detalles Bibliográficos
Autores principales: Sysoev, Oleg, Bartoszek, Krzysztof, Ekström, Eva‐Charlotte, Ekholm Selling, Katarina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771862/
https://www.ncbi.nlm.nih.gov/pubmed/31246349
http://dx.doi.org/10.1002/sim.8308
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author Sysoev, Oleg
Bartoszek, Krzysztof
Ekström, Eva‐Charlotte
Ekholm Selling, Katarina
author_facet Sysoev, Oleg
Bartoszek, Krzysztof
Ekström, Eva‐Charlotte
Ekholm Selling, Katarina
author_sort Sysoev, Oleg
collection PubMed
description 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.
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spelling pubmed-67718622019-10-07 PSICA: Decision trees for probabilistic subgroup identification with categorical treatments Sysoev, Oleg Bartoszek, Krzysztof Ekström, Eva‐Charlotte Ekholm Selling, Katarina Stat Med Research Articles 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. John Wiley and Sons Inc. 2019-06-27 2019-09-30 /pmc/articles/PMC6771862/ /pubmed/31246349 http://dx.doi.org/10.1002/sim.8308 Text en © 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Sysoev, Oleg
Bartoszek, Krzysztof
Ekström, Eva‐Charlotte
Ekholm Selling, Katarina
PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
title PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
title_full PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
title_fullStr PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
title_full_unstemmed PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
title_short PSICA: Decision trees for probabilistic subgroup identification with categorical treatments
title_sort psica: decision trees for probabilistic subgroup identification with categorical treatments
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6771862/
https://www.ncbi.nlm.nih.gov/pubmed/31246349
http://dx.doi.org/10.1002/sim.8308
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