Evaluating the Effectiveness of Personalized Medicine With Software.
bootstrap
inference
personalized medicine
randomized comparative trial
statistical software
treatment regimes
Journal
Frontiers in big data
ISSN: 2624-909X
Titre abrégé: Front Big Data
Pays: Switzerland
ID NLM: 101770603
Informations de publication
Date de publication:
2021
2021
Historique:
received:
14
06
2020
accepted:
03
02
2021
entrez:
4
6
2021
pubmed:
5
6
2021
medline:
5
6
2021
Statut:
epublish
Résumé
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
Identifiants
pubmed: 34085036
doi: 10.3389/fdata.2021.572532
pii: 572532
pmc: PMC8167073
doi:
Types de publication
Journal Article
Langues
eng
Pagination
572532Informations de copyright
Copyright © 2021 Kapelner, Bleich, Levine, Cohen, DeRubeis and Berk.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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