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Methods for handling longitudinal outcome processes truncated by dropout and death

Cohort data are often incomplete because some subjects drop out of the study, and inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods that deal with such missing data. In cohort studies of ageing, missing data can arise from dropout or death. Methods...

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Detalles Bibliográficos
Autores principales: Wen, Lan, Terrera, Graciela Muniz, Seaman, Shaun R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5971107/
https://www.ncbi.nlm.nih.gov/pubmed/29028922
http://dx.doi.org/10.1093/biostatistics/kxx045
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author Wen, Lan
Terrera, Graciela Muniz
Seaman, Shaun R
author_facet Wen, Lan
Terrera, Graciela Muniz
Seaman, Shaun R
author_sort Wen, Lan
collection PubMed
description Cohort data are often incomplete because some subjects drop out of the study, and inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods that deal with such missing data. In cohort studies of ageing, missing data can arise from dropout or death. Methods that do not distinguish between these reasons for missingness typically provide inference about a hypothetical cohort where no one can die (immortal cohort). It has been suggested that inference about the cohort composed of those who are still alive at any time point (partly conditional inference) may be more meaningful. MI, LI, and IPW can all be adapted to provide partly conditional inference. In this article, we clarify and compare the assumptions required by these MI, LI, and IPW methods for partly conditional inference on continuous outcomes. We also propose augmented IPW estimators for making partly conditional inference. These are more efficient than IPW estimators and more robust to model misspecification. Our simulation studies show that the methods give approximately unbiased estimates of partly conditional estimands when their assumptions are met, but may be biased otherwise. We illustrate the application of the missing data methods using data from the ‘Origins of Variance in the Old–old’ Twin study.
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spelling pubmed-59711072018-10-13 Methods for handling longitudinal outcome processes truncated by dropout and death Wen, Lan Terrera, Graciela Muniz Seaman, Shaun R Biostatistics Articles Cohort data are often incomplete because some subjects drop out of the study, and inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods that deal with such missing data. In cohort studies of ageing, missing data can arise from dropout or death. Methods that do not distinguish between these reasons for missingness typically provide inference about a hypothetical cohort where no one can die (immortal cohort). It has been suggested that inference about the cohort composed of those who are still alive at any time point (partly conditional inference) may be more meaningful. MI, LI, and IPW can all be adapted to provide partly conditional inference. In this article, we clarify and compare the assumptions required by these MI, LI, and IPW methods for partly conditional inference on continuous outcomes. We also propose augmented IPW estimators for making partly conditional inference. These are more efficient than IPW estimators and more robust to model misspecification. Our simulation studies show that the methods give approximately unbiased estimates of partly conditional estimands when their assumptions are met, but may be biased otherwise. We illustrate the application of the missing data methods using data from the ‘Origins of Variance in the Old–old’ Twin study. Oxford University Press 2018-10 2017-09-26 /pmc/articles/PMC5971107/ /pubmed/29028922 http://dx.doi.org/10.1093/biostatistics/kxx045 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Wen, Lan
Terrera, Graciela Muniz
Seaman, Shaun R
Methods for handling longitudinal outcome processes truncated by dropout and death
title Methods for handling longitudinal outcome processes truncated by dropout and death
title_full Methods for handling longitudinal outcome processes truncated by dropout and death
title_fullStr Methods for handling longitudinal outcome processes truncated by dropout and death
title_full_unstemmed Methods for handling longitudinal outcome processes truncated by dropout and death
title_short Methods for handling longitudinal outcome processes truncated by dropout and death
title_sort methods for handling longitudinal outcome processes truncated by dropout and death
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5971107/
https://www.ncbi.nlm.nih.gov/pubmed/29028922
http://dx.doi.org/10.1093/biostatistics/kxx045
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