New insights into handling missing values in environmental epidemiological studies.

Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bay...

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Autores principales:	Célina Roda, Ioannis Nicolis, Isabelle Momas, Chantal Guihenneuc
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3030e663849452381ff11ce32acb1e3
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spelling	oai:doaj.org-article:e3030e663849452381ff11ce32acb1e32021-11-25T06:00:29ZNew insights into handling missing values in environmental epidemiological studies.1932-620310.1371/journal.pone.0104254https://doaj.org/article/e3030e663849452381ff11ce32acb1e32014-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0104254https://doaj.org/toc/1932-6203Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bayesian approach. These methods were applied to the PARIS birth cohort, where indoor domestic pollutant measurements were performed in a random sample of babies' dwellings. A simulation study was conducted to assess performances of different approaches with a high proportion of missing values (from 50% to 95%). Different simulation scenarios were carried out, controlling the true value of the association (odds ratio of 1.0, 1.2, and 1.4), and varying the health outcome prevalence. When a large amount of data is missing, omitting these missing data reduced statistical power and inflated standard errors, which affected the significance of the association. Single imputation underestimated the variability, and considerably increased risk of type I error. All approaches were conservative, except the Bayesian joint model. In the case of a common health outcome, the fully Bayesian approach is the most efficient approach (low root mean square error, reasonable type I error, and high statistical power). Nevertheless for a less prevalent event, the type I error is increased and the statistical power is reduced. The estimated posterior distribution of the OR is useful to refine the conclusion. Among the methods handling missing values, no approach is absolutely the best but when usual approaches (e.g. single imputation) are not sufficient, joint modelling approach of missing process and health association is more efficient when large amounts of data are missing.Célina RodaIoannis NicolisIsabelle MomasChantal GuihenneucPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e104254 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Célina Roda Ioannis Nicolis Isabelle Momas Chantal Guihenneuc New insights into handling missing values in environmental epidemiological studies.
description	Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bayesian approach. These methods were applied to the PARIS birth cohort, where indoor domestic pollutant measurements were performed in a random sample of babies' dwellings. A simulation study was conducted to assess performances of different approaches with a high proportion of missing values (from 50% to 95%). Different simulation scenarios were carried out, controlling the true value of the association (odds ratio of 1.0, 1.2, and 1.4), and varying the health outcome prevalence. When a large amount of data is missing, omitting these missing data reduced statistical power and inflated standard errors, which affected the significance of the association. Single imputation underestimated the variability, and considerably increased risk of type I error. All approaches were conservative, except the Bayesian joint model. In the case of a common health outcome, the fully Bayesian approach is the most efficient approach (low root mean square error, reasonable type I error, and high statistical power). Nevertheless for a less prevalent event, the type I error is increased and the statistical power is reduced. The estimated posterior distribution of the OR is useful to refine the conclusion. Among the methods handling missing values, no approach is absolutely the best but when usual approaches (e.g. single imputation) are not sufficient, joint modelling approach of missing process and health association is more efficient when large amounts of data are missing.
format	article
author	Célina Roda Ioannis Nicolis Isabelle Momas Chantal Guihenneuc
author_facet	Célina Roda Ioannis Nicolis Isabelle Momas Chantal Guihenneuc
author_sort	Célina Roda
title	New insights into handling missing values in environmental epidemiological studies.
title_short	New insights into handling missing values in environmental epidemiological studies.
title_full	New insights into handling missing values in environmental epidemiological studies.
title_fullStr	New insights into handling missing values in environmental epidemiological studies.
title_full_unstemmed	New insights into handling missing values in environmental epidemiological studies.
title_sort	new insights into handling missing values in environmental epidemiological studies.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/e3030e663849452381ff11ce32acb1e3
work_keys_str_mv	AT celinaroda newinsightsintohandlingmissingvaluesinenvironmentalepidemiologicalstudies AT ioannisnicolis newinsightsintohandlingmissingvaluesinenvironmentalepidemiologicalstudies AT isabellemomas newinsightsintohandlingmissingvaluesinenvironmentalepidemiologicalstudies AT chantalguihenneuc newinsightsintohandlingmissingvaluesinenvironmentalepidemiologicalstudies
_version_	1718414311910539264

New insights into handling missing values in environmental epidemiological studies.

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