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Mixed Effects Models with Censored Covariates, with Applications in HIVAIDS Studies
Joint Authors
Source
Journal of Probability and Statistics
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-03
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Mixed effects models are widely used for modelling clustered data when there are large variations between clusters, since mixed effects models allow for cluster-specific inference.
In some longitudinal studies such as HIV/AIDS studies, it is common that some time-varying covariates may be left or right censored due to detection limits, may be missing at times of interest, or may be measured with errors.
To address these “incomplete data“ problems, a common approach is to model the time-varying covariates based on observed covariate data and then use the fitted model to “predict” the censored or missing or mismeasured covariates.
In this article, we provide a review of the common approaches for censored covariates in longitudinal and survival response models and advocate nonlinear mechanistic covariate models if such models are available.
American Psychological Association (APA)
Wu, Lang& Zhang, Hongbin. 2018. Mixed Effects Models with Censored Covariates, with Applications in HIVAIDS Studies. Journal of Probability and Statistics،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1197663
Modern Language Association (MLA)
Wu, Lang& Zhang, Hongbin. Mixed Effects Models with Censored Covariates, with Applications in HIVAIDS Studies. Journal of Probability and Statistics No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1197663
American Medical Association (AMA)
Wu, Lang& Zhang, Hongbin. Mixed Effects Models with Censored Covariates, with Applications in HIVAIDS Studies. Journal of Probability and Statistics. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1197663
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197663