New Insights into Handling Missing Values in Environmental Epidemiological Studies

International audienceMissing 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

Robustness of the BYM model in absence of spatial variation in the residuals

<p>Abstract</p> <p>Background</p> <p>In the context of ecological studies, the Bayesian hierarchical Poisson model is of prime interest when studying the association between environmental exposure and rare diseases. However, adding spatially structured extra-variability in the model fitted to the data when such extra-variability does not exist conditionally on the covariates included in the model (<it>over-fitting</it>) may bias the estimation of the ecological association between covariates and relative risks toward the null. In order to investigate that possibility, a simulation study of the impact of introducing unnecessary residual spatial structure in the estimation model was conducted.</p> <p>Results</p> <p>In the case where no underlying extra-variability from the Poisson process exists, the simulation results show that models accounting for structured and unstructured residuals do not underestimate the ecological association, unless covariates have a very strong autocorrelation structure, i.e., 0.98 at 100 km on a territory of diameter 1000 km."</p

Excess mortality related to the August 2003 heat wave in France.

Objectives: From August 1st to 20th, 2003, the mean maximum temperature in France exceeded the seasonal norm by 11-12 degrees C on nine consecutive days. A major increase in mortality was then observed, which main epidemiological features are described herein. Methods: The number of deaths observed from August to November 2003 in France was compared to those expected on the basis of the mortality rates observed from 2000 to 2002 and the 2003 population estimates. Results: From August 1st to 20th, 2003, 15,000 excess deaths were observed. From 35 years age, the excess mortality was marked and increased with age. It was 15% higher in women than in men of comparable age as of age 45 years. Excess mortality at home and in retirement institutions was greater than that in hospitals. The mortality of widowed, single and divorced subjects was greater than that of married people. Deaths directly related to heat, heatstroke, hyperthermia and dehydration increased massively. Cardiovascular diseases, ill-defined morbid disorders, respiratory diseases and nervous system diseases also markedly contributed to the excess mortality. The geographic variations in mortality showed a clear age-dependent relationship with the number of very hot days. No harvesting effect was observed. Conclusions: Heat waves must be considered as a threat to European populations living in climates that are currently temperate. While the elderly and people living alone are particularly vulnerable to heat waves, no segment of the population may be considered protected from the risks associated with heat waves

Recentered importance sampling with applications to Bayesian model validation

Since its introduction in the early 1990s, the idea of using importance sampling (IS) with Markov chain Monte Carlo (MCMC) has found many applications. This article examines problems associated with its application to repeated evaluation of related posterior distributions with a particular focus on Bayesian model validation. We demonstrate that, in certain applications, the curse of dimensionality can be reduced by a simple modification of IS. In addition to providing new theoretical insight into the behavior of the IS approximation in a wide class of models, our result facilitates the implementation of computationally intensive Bayesian model checks. We illustrate the simplicity, computational savings, and potential inferential advantages of the proposed approach through two substantive case studies, notably computation of Bayesian p-values for linear regression models and simulation-based model checking. Supplementary materials including the Appendix and the R code for Section 3.1.2 are available online

Dynamics of the Risk of Smoking-Induced Lung Cancer A Compartmental Hidden Markov Model for Longitudinal Analysis

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Formaldehyde Exposure and Lower Respiratory Infections in Infants: Findings from the PARIS Cohort Study

Background: Certain chemical pollutants can exacerbate lower respiratory tract infections (LRIs), a common childhood ailment. Although formaldehyde (FA) is one of the most common air pollutants found in indoor environments, its impact on infant health is uncertain

Test du rapport des maximums de vraisemblance: - Emploi en genetique; - detections de rupture dans les modeles de regression lineaire; application au SIDA

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : TD 80959 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Modélisation statistique des variations géographiques : enjeu d'importance en épidémiologie et en statistique

National audienc

Combining expert opinions in prior elicitation

A method of eliciting prior distributions for Bayesian models using expert knowledge is proposed. Elicitation is a widely studied problem, from a psychological perspective as well as from a statistical perspective. Here, we are interested in combining opinions from more than one expert using an explicitly model-based approach so that we may account for various sources of variation affecting elicited expert opinions. We use a hierarchical model to achieve this. We apply this approach to two problems. The first problem involves a food risk assessment problem involving modelling dose-response for Listeria monocytogenes contamination of mice. The second concerns the time taken by PhD students to submit their thesis in a particular school

Spatial regression: a flexible bayesian model for the autocorrelated error structure

International audienc