A two-part mixed-effects pattern mixture model to handle zero-inflation and incompleteness in a longitudinal setting
A two-part mixed-effects pattern mixture model to handle zero-inflation and incompleteness in a longitudinal setting
Two-part regression models are frequently used to analyze longitudinal count data with excess zeros, where the same set of subjects is repeatedly observed over time. In this context, several sources of heterogeneity may arise at individual level that affect the observed process. Further, longitudinal studies often suffer from missing values: individuals dropout of the study before its completion, and thus present incomplete data records. In this paper, we propose a finite mixture of hurdle models to face the heterogeneity problem, which is handled by introducing random effects with a discrete distribution; a pattern-mixture approach is specified to deal with non-ignorable missing values. This approach helps us to consider overdispersed counts, while allowing for association between the two parts of the model, and for non-ignorable dropouts. The effectiveness of the proposal is tested through a simulation study. Finally, an application to real data on skin cancer is provided.
716-734
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
24 August 2011
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Maruotti, Antonello
(2011)
A two-part mixed-effects pattern mixture model to handle zero-inflation and incompleteness in a longitudinal setting.
Biometrical Journal, 53 (5), .
(doi:10.1002/bimj.201000190).
(PMID:21887792)
Abstract
Two-part regression models are frequently used to analyze longitudinal count data with excess zeros, where the same set of subjects is repeatedly observed over time. In this context, several sources of heterogeneity may arise at individual level that affect the observed process. Further, longitudinal studies often suffer from missing values: individuals dropout of the study before its completion, and thus present incomplete data records. In this paper, we propose a finite mixture of hurdle models to face the heterogeneity problem, which is handled by introducing random effects with a discrete distribution; a pattern-mixture approach is specified to deal with non-ignorable missing values. This approach helps us to consider overdispersed counts, while allowing for association between the two parts of the model, and for non-ignorable dropouts. The effectiveness of the proposal is tested through a simulation study. Finally, an application to real data on skin cancer is provided.
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Published date: 24 August 2011
Organisations:
Statistics, Statistical Sciences Research Institute
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Local EPrints ID: 341229
URI: http://eprints.soton.ac.uk/id/eprint/341229
ISSN: 0323-3847
PURE UUID: f6e05de7-fb88-45ea-8e2e-e2c71d06d447
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Date deposited: 18 Jul 2012 14:21
Last modified: 14 Mar 2024 11:36
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Author:
Antonello Maruotti
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