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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Main Authors: Adam Kapelner, Justin Bleich, Alina Levine, Zachary D. Cohen, Robert J. DeRubeis, Richard Berk
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-05-01
Series:Frontiers in Big Data
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fdata.2021.572532/full
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spelling doaj-73b2f78f4da7461085359470258508e02021-05-18T04:19:05ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2021-05-01410.3389/fdata.2021.572532572532Evaluating the Effectiveness of Personalized Medicine With SoftwareAdam Kapelner0Justin Bleich1Alina Levine2Zachary D. Cohen3Robert J. DeRubeis4Richard Berk5Department of Mathematics, Queens College, CUNY, Queens, NY, United StatesDepartment of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Mathematics, Queens College, CUNY, Queens, NY, United StatesDepartment of Psychology, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Psychology, University of Pennsylvania, Philadelphia, PA, United StatesDepartment of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, PA, United StatesWe 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.https://www.frontiersin.org/articles/10.3389/fdata.2021.572532/fullpersonalized medicineinferencebootstraptreatment regimesrandomized comparative trialstatistical software
collection DOAJ
language English
format Article
sources DOAJ
author Adam Kapelner
Justin Bleich
Alina Levine
Zachary D. Cohen
Robert J. DeRubeis
Richard Berk
spellingShingle Adam Kapelner
Justin Bleich
Alina Levine
Zachary D. Cohen
Robert J. DeRubeis
Richard Berk
Evaluating the Effectiveness of Personalized Medicine With Software
Frontiers in Big Data
personalized medicine
inference
bootstrap
treatment regimes
randomized comparative trial
statistical software
author_facet Adam Kapelner
Justin Bleich
Alina Levine
Zachary D. Cohen
Robert J. DeRubeis
Richard Berk
author_sort Adam Kapelner
title Evaluating the Effectiveness of Personalized Medicine With Software
title_short 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_sort evaluating the effectiveness of personalized medicine with software
publisher Frontiers Media S.A.
series Frontiers in Big Data
issn 2624-909X
publishDate 2021-05-01
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.
topic personalized medicine
inference
bootstrap
treatment regimes
randomized comparative trial
statistical software
url https://www.frontiersin.org/articles/10.3389/fdata.2021.572532/full
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