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Tytuł:
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Distribution-free models for latent mixed population responses in a longitudinal setting with missing data.
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Autorzy:
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Zhang H; Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
Tang L; Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
Kong Y; Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
Chen T; Department of Mathematics and Statistics, University of Toledo, Toledo, OH, USA.
Liu X; Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
Zhang Z; Department of Statistics, University of California, Riverside, CA, USA.
Zhang B; Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.
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Źródło:
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Statistical methods in medical research [Stat Methods Med Res] 2019 Oct-Nov; Vol. 28 (10-11), pp. 3273-3285. Date of Electronic Publication: 2018 Sep 24.
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Publication: London : SAGE Publications
Original Publication: Sevenoaks, Kent, UK : Edward Arnold, c1992-
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MeSH Terms:
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Longitudinal Studies*
Models, Statistical*
Research Subjects/*statistics & numerical data
Biometry ; Female ; HIV Infections/prevention & control ; Humans ; Male ; Unsafe Sex
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Contributed Indexing:
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Keywords: Latent population mixture; inverse probability weight; longitudinal response; missing data; non-parametric
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Entry Date(s):
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Date Created: 20180925 Date Completed: 20201209 Latest Revision: 20201214
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Update Code:
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20240105
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DOI:
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10.1177/0962280218801123
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PMID:
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30246608
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Many biomedical and psychosocial studies involve population mixtures, which consist of multiple latent subpopulations. Because group membership cannot be observed, standard methods do not apply when differential treatment effects need to be studied across subgroups. We consider a two-group mixture in which membership of latent subgroups is determined by structural zeroes of a zero-inflated count variable and propose a new approach to model treatment differences between latent subgroups in a longitudinal setting. It has also been incorporated with the inverse probability weighted method to address data missingness. As the approach builds on the distribution-free functional response models, it requires no parametric distribution model and thereby provides a robust inference. We illustrate the approach with both real and simulated data.