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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Frontiers Media S.A.
2021-05-01
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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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