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Two-Part and Related Regression Models for Longitudinal Data
Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5590716/ https://www.ncbi.nlm.nih.gov/pubmed/28890906 http://dx.doi.org/10.1146/annurev-statistics-060116-054131 |
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author | Farewell, V.T. Long, D.L. Tom, B.D.M. Yiu, S. Su, L. |
author_facet | Farewell, V.T. Long, D.L. Tom, B.D.M. Yiu, S. Su, L. |
author_sort | Farewell, V.T. |
collection | PubMed |
description | Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution. |
format | Online Article Text |
id | pubmed-5590716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
record_format | MEDLINE/PubMed |
spelling | pubmed-55907162017-09-08 Two-Part and Related Regression Models for Longitudinal Data Farewell, V.T. Long, D.L. Tom, B.D.M. Yiu, S. Su, L. Annu Rev Stat Appl Article Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution. 2017-03 /pmc/articles/PMC5590716/ /pubmed/28890906 http://dx.doi.org/10.1146/annurev-statistics-060116-054131 Text en https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third party material in this article for license information. |
spellingShingle | Article Farewell, V.T. Long, D.L. Tom, B.D.M. Yiu, S. Su, L. Two-Part and Related Regression Models for Longitudinal Data |
title | Two-Part and Related Regression Models for Longitudinal Data |
title_full | Two-Part and Related Regression Models for Longitudinal Data |
title_fullStr | Two-Part and Related Regression Models for Longitudinal Data |
title_full_unstemmed | Two-Part and Related Regression Models for Longitudinal Data |
title_short | Two-Part and Related Regression Models for Longitudinal Data |
title_sort | two-part and related regression models for longitudinal data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5590716/ https://www.ncbi.nlm.nih.gov/pubmed/28890906 http://dx.doi.org/10.1146/annurev-statistics-060116-054131 |
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