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Evaluating the Effectiveness of Personalized Medicine With Software

We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a...

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Autores principales: Kapelner, Adam, Bleich, Justin, Levine, Alina, Cohen, Zachary D., DeRubeis, Robert J., Berk, Richard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8167073/
https://www.ncbi.nlm.nih.gov/pubmed/34085036
http://dx.doi.org/10.3389/fdata.2021.572532
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author Kapelner, Adam
Bleich, Justin
Levine, Alina
Cohen, Zachary D.
DeRubeis, Robert J.
Berk, Richard
author_facet Kapelner, Adam
Bleich, Justin
Levine, Alina
Cohen, Zachary D.
DeRubeis, Robert J.
Berk, Richard
author_sort Kapelner, Adam
collection PubMed
description We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better average outcome for the patient. Why is personalized medicine not done more in practice? One of many reasons is because practitioners do not have any easy way to holistically evaluate whether their personalization procedure does better than the standard of care, termed improvement. Our software, “Personalized Treatment Evaluator” (the R package PTE), provides inference for improvement out-of-sample in many clinical scenarios. We also extend current methodology by allowing evaluation of improvement in the case where the endpoint is binary or survival. In the software, the practitioner inputs 1) data from a single-stage randomized trial with one continuous, incidence or survival endpoint and 2) an educated guess of a functional form of a model for the endpoint constructed from domain knowledge. The bootstrap is then employed on data unseen during model fitting to provide confidence intervals for the improvement for the average future patient (assuming future patients are similar to the patients in the trial). One may also test against a null scenario where the hypothesized personalization are not more useful than a standard of care. We demonstrate our method’s promise on simulated data as well as on data from a randomized comparative trial investigating two treatments for depression.
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spelling pubmed-81670732021-06-02 Evaluating the Effectiveness of Personalized Medicine With Software Kapelner, Adam Bleich, Justin Levine, Alina Cohen, Zachary D. DeRubeis, Robert J. Berk, Richard Front Big Data Big Data We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better average outcome for the patient. Why is personalized medicine not done more in practice? One of many reasons is because practitioners do not have any easy way to holistically evaluate whether their personalization procedure does better than the standard of care, termed improvement. Our software, “Personalized Treatment Evaluator” (the R package PTE), provides inference for improvement out-of-sample in many clinical scenarios. We also extend current methodology by allowing evaluation of improvement in the case where the endpoint is binary or survival. In the software, the practitioner inputs 1) data from a single-stage randomized trial with one continuous, incidence or survival endpoint and 2) an educated guess of a functional form of a model for the endpoint constructed from domain knowledge. The bootstrap is then employed on data unseen during model fitting to provide confidence intervals for the improvement for the average future patient (assuming future patients are similar to the patients in the trial). One may also test against a null scenario where the hypothesized personalization are not more useful than a standard of care. We demonstrate our method’s promise on simulated data as well as on data from a randomized comparative trial investigating two treatments for depression. Frontiers Media S.A. 2021-05-18 /pmc/articles/PMC8167073/ /pubmed/34085036 http://dx.doi.org/10.3389/fdata.2021.572532 Text en Copyright © 2021 Kapelner, Bleich, Levine, Cohen, DeRubeis and Berk. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Kapelner, Adam
Bleich, Justin
Levine, Alina
Cohen, Zachary D.
DeRubeis, Robert J.
Berk, Richard
Evaluating the Effectiveness of Personalized Medicine With Software
title Evaluating the Effectiveness of Personalized Medicine With Software
title_full Evaluating the Effectiveness of Personalized Medicine With Software
title_fullStr Evaluating the Effectiveness of Personalized Medicine With Software
title_full_unstemmed Evaluating the Effectiveness of Personalized Medicine With Software
title_short Evaluating the Effectiveness of Personalized Medicine With Software
title_sort evaluating the effectiveness of personalized medicine with software
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8167073/
https://www.ncbi.nlm.nih.gov/pubmed/34085036
http://dx.doi.org/10.3389/fdata.2021.572532
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