title: New insights into handling missing values in environmental epidemiological studies.
abstract: 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.
authors.affiliation: Laboratoire Santé Publique et Environnement, EA 4064, Faculté de Pharmacie, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.
authors.forename: Célina
authors.initials: C
authors.lastname: Roda
authors.affiliation: Laboratoire Santé Publique et Environnement, EA 4064, Faculté de Pharmacie, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Nicolis
authors.affiliation: Laboratoire Santé Publique et Environnement, EA 4064, Faculté de Pharmacie, Université Paris Descartes, Sorbonne Paris Cité, Paris, France; Mairie de Paris, Direction de l'Action Sociale de l'Enfance et de la Santé, Cellule Cohorte, Paris, France.
authors.forename: Isabelle
authors.initials: I
authors.lastname: Momas
authors.affiliation: Laboratoire Santé Publique et Environnement, EA 4064, Faculté de Pharmacie, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.
authors.forename: Chantal
authors.initials: C
authors.lastname: Guihenneuc
mesh_terms: D001499:Bayes Theorem; D066264:Datasets as Topic; D004781:Environmental Exposure; D004784:Environmental Monitoring; D016021:Epidemiologic Studies; D006801:Humans; D008962:Models, Theoretical; D012044:Regression Analysis; D012107:Research Design