On the Addams family of discrete frailty distributions for modeling multivariate case I interval-censored data.

Autor:	Bardo M; Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, Göttingen 37073, Germany., Hens N; I-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, Hasselt 3500, Belgium.; Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Antwerpen 2610, Belgium., Unkel S; Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee 32, Göttingen 37073, Germany.; Faculty V: School of Life Sciences, University of Siegen, Am Eichenhang 50, Siegen 57076, Germany.
Jazyk:	angličtina
Zdroj:	Biostatistics (Oxford, England) [Biostatistics] 2024 Sep 10. Date of Electronic Publication: 2024 Sep 10.
DOI:	10.1093/biostatistics/kxae035
Abstrakt:	Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or impossible to measure. In the literature, the random effect is usually assumed to have a continuous distribution. However, in some areas of application, discrete frailty distributions may be more appropriate. The present paper is about the implementation and interpretation of the Addams family of discrete frailty distributions. We propose methods of estimation for this family of densities in the context of shared frailty models for the hazard rates for case I interval-censored data. Our optimization framework allows for stratification of random effect distributions by covariates. We highlight interpretational advantages of the Addams family of discrete frailty distributions and theK-point distribution as compared to other frailty distributions. A unique feature of the Addams family and the K-point distribution is that the support of the frailty distribution depends on its parameters. This feature is best exploited by imposing a model on the distributional parameters, resulting in a model with non-homogeneous covariate effects that can be analyzed using standard measures such as the hazard ratio. Our methods are illustrated with applications to multivariate case I interval-censored infection data. (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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