Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. sq...

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Main Authors:	Bartlett, Jonathan W, Seaman, Shaun R, White, Ian R, Carpenter, James R
Format:	Online
Language:	English
Published:	SAGE Publications 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4513015/

id	pubmed-4513015
recordtype	oai_dc
spelling	pubmed-45130152015-07-31 Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model Bartlett, Jonathan W Seaman, Shaun R White, Ian R Carpenter, James R Articles Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. Stata software implementing the approach is freely available. SAGE Publications 2015-08 /pmc/articles/PMC4513015/ /pubmed/24525487 http://dx.doi.org/10.1177/0962280214521348 Text en © The Author(s) 2014 http://creativecommons.org/licenses/by/3.0/ This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (http://www.uk.sagepub.com/aboutus/openaccess.htm).
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	Bartlett, Jonathan W Seaman, Shaun R White, Ian R Carpenter, James R
spellingShingle	Bartlett, Jonathan W Seaman, Shaun R White, Ian R Carpenter, James R Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
author_facet	Bartlett, Jonathan W Seaman, Shaun R White, Ian R Carpenter, James R
author_sort	Bartlett, Jonathan W
title	Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
title_short	Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
title_full	Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
title_fullStr	Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
title_full_unstemmed	Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model
title_sort	multiple imputation of covariates by fully conditional specification: accommodating the substantive model
description	Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. Stata software implementing the approach is freely available.
publisher	SAGE Publications
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4513015/
_version_	1613251457132265472

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

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