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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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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. |
format | Online Article Text |
id | pubmed-5971107 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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