Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation

The Cox proportional hazards model is frequently used in medical statistics. The standard methods for fitting this model rely on the assumption of independent censoring. Although this is sometimes plausible, we often wish to explore how robust our inferences are as this untestable assumption is rela...

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Main Authors:	Jackson, Dan, White, Ian R, Seaman, Shaun, Evans, Hannah, Baisley, Kathy, Carpenter, James
Format:	Online
Language:	English
Published:	BlackWell Publishing Ltd 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282781/

id	pubmed-4282781
recordtype	oai_dc
spelling	pubmed-42827812015-01-15 Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation Jackson, Dan White, Ian R Seaman, Shaun Evans, Hannah Baisley, Kathy Carpenter, James Research Articles The Cox proportional hazards model is frequently used in medical statistics. The standard methods for fitting this model rely on the assumption of independent censoring. Although this is sometimes plausible, we often wish to explore how robust our inferences are as this untestable assumption is relaxed. We describe how this can be carried out in a way that makes the assumptions accessible to all those involved in a research project. Estimation proceeds via multiple imputation, where censored failure times are imputed under user-specified departures from independent censoring. A novel aspect of our method is the use of bootstrapping to generate proper imputations from the Cox model. We illustrate our approach using data from an HIV-prevention trial and discuss how it can be readily adapted and applied in other settings. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. BlackWell Publishing Ltd 2014-11-30 2014-07-25 /pmc/articles/PMC4282781/ /pubmed/25060703 http://dx.doi.org/10.1002/sim.6274 Text en © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Jackson, Dan White, Ian R Seaman, Shaun Evans, Hannah Baisley, Kathy Carpenter, James
spellingShingle	Jackson, Dan White, Ian R Seaman, Shaun Evans, Hannah Baisley, Kathy Carpenter, James Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation
author_facet	Jackson, Dan White, Ian R Seaman, Shaun Evans, Hannah Baisley, Kathy Carpenter, James
author_sort	Jackson, Dan
title	Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation
title_short	Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation
title_full	Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation
title_fullStr	Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation
title_full_unstemmed	Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation
title_sort	relaxing the independent censoring assumption in the cox proportional hazards model using multiple imputation
description	The Cox proportional hazards model is frequently used in medical statistics. The standard methods for fitting this model rely on the assumption of independent censoring. Although this is sometimes plausible, we often wish to explore how robust our inferences are as this untestable assumption is relaxed. We describe how this can be carried out in a way that makes the assumptions accessible to all those involved in a research project. Estimation proceeds via multiple imputation, where censored failure times are imputed under user-specified departures from independent censoring. A novel aspect of our method is the use of bootstrapping to generate proper imputations from the Cox model. We illustrate our approach using data from an HIV-prevention trial and discuss how it can be readily adapted and applied in other settings. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
publisher	BlackWell Publishing Ltd
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4282781/
_version_	1613172835838066688

Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation

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