Healthcare center clustering for Cox's proportional hazards model by fusion penalty.
fixed effects
fusion penalty
latent class
provider profiling
random effects
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
10 09 2023
10 09 2023
Historique:
revised:
31
03
2023
received:
21
04
2022
accepted:
01
06
2023
pmc-release:
10
09
2024
medline:
21
8
2023
pubmed:
15
6
2023
entrez:
14
6
2023
Statut:
ppublish
Résumé
There has been growing research interest in developing methodology to evaluate healthcare centers' performance with respect to patient outcomes. Conventional assessments can be conducted using fixed or random effects models, as seen in provider profiling. We propose a new method, using fusion penalty to cluster healthcare centers with respect to a survival outcome. Without any prior knowledge of the grouping information, the new method provides a desirable data-driven approach for automatically clustering healthcare centers into distinct groups based on their performance. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. The validity of our approach is demonstrated through simulation studies, and its practical application is illustrated by analyzing data from the national kidney transplant registry.
Identifiants
pubmed: 37315935
doi: 10.1002/sim.9825
pmc: PMC10530598
mid: NIHMS1923699
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
3685-3698Subventions
Organisme : NIA NIH HHS
ID : R21 AG063370
Pays : United States
Organisme : NEI NIH HHS
ID : R21 EY031884
Pays : United States
Organisme : NEI NIH HHS
ID : R21 EY033518
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002345
Pays : United States
Informations de copyright
© 2023 John Wiley & Sons Ltd.
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