Mixture proportional hazards cure model with latent variables.
cure model
factor analysis
latent variables
logistic regression
proportional hazards model
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
20 12 2021
20 12 2021
Historique:
revised:
19
08
2021
received:
30
04
2020
accepted:
30
08
2021
pubmed:
17
9
2021
medline:
11
3
2022
entrez:
16
9
2021
Statut:
ppublish
Résumé
A mixture proportional hazards cure model with latent variables is proposed. The proposed model assesses the effects of the observed and latent risk factors on the hazards of uncured subjects and the cure rate through a proportional hazards model and a logistic model, respectively. Factor analysis is employed to measure the latent variables through correlated multiple indicators. Maximum likelihood estimation is performed through a Gaussian quadratic technique that approximates the integration over the latent variables. A piecewise constant function is used for the unspecified baseline hazard of uncured subjects. The proposed method can be conveniently implemented by using SAS Proc NLMIXED. Simulation studies are conducted to evaluate the performance of the proposed approach. An application to a study concerning the risk factors of chronic kidney disease for type 2 diabetic patients is provided.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6590-6604Informations de copyright
© 2021 John Wiley & Sons Ltd.
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