A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data.
censored survival data
gradient tree boosting
nonparametric
personalized medicine
restricted mean survival time
subgroup identification
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
10 12 2020
10 12 2020
Historique:
received:
09
02
2020
revised:
08
06
2020
accepted:
09
07
2020
pubmed:
14
8
2020
medline:
22
6
2021
entrez:
14
8
2020
Statut:
ppublish
Résumé
In randomized clinical trials with survival outcome, there has been an increasing interest in subgroup identification based on baseline genomic, proteomic markers, or clinical characteristics. Some of the existing methods identify subgroups that benefit substantially from the experimental treatment by directly modeling outcomes or treatment effect. When the goal is to find an optimal treatment for a given patient rather than finding the right patient for a given treatment, methods under the individualized treatment regime framework estimate an individualized treatment rule that would lead to the best expected clinical outcome as measured by a value function. Connecting the concept of value function to subgroup identification, we propose a nonparametric method that searches for subgroup membership scores by maximizing a value function that directly reflects the subgroup-treatment interaction effect based on restricted mean survival time. A gradient tree boosting algorithm is proposed to search for the individual subgroup membership scores. We conduct simulation studies to evaluate the performance of the proposed method and an application to an AIDS clinical trial is performed for illustration.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4133-4146Informations de copyright
© 2020 John Wiley & Sons, Ltd.
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