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

Impact of Cliff and Ord (1969, 1981) on Spatial Epidemiology

The influence in spatial epidemiology of the seminar work on autocorrelation by Cliff and Ord is discussed. Quantifying the evidence of spatial clustering was an important step in the development of modern statistical methods for analyzing spatial variations of diseases. Autocorrelation is nowadays mostly accounted for at a latent level within a hierarchical framework to small area disease mapping. The importance of accounting for autocorrelation in geographical correlation studies is also reviewed

A cautionary comment on the generation of Berkson error in epidemiological studies

International audienceExposure measurement error can be seen as one of the most important sources of uncertainty in studies in epidemiology. When the aim is to assess the effects of measurement error on statistical inference or to compare the performance of several methods for measurement error correction, it is indispensable to be able to generate different types of measurement error. This paper compares two approaches for the generation of Berkson error, which have recently been applied in radiation epidemiology, in their ability to generate exposure data that satisfy the properties of the Berkson model. In particular, it is shown that the use of one of the methods produces results that are not in accordance with two important properties of Berkson error. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature

Dynamics of the spread of the Bovine Viral Diarrhoea Virus (BVDV) within a dairy herd according to the separation of animals into subgroups

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Accounting for Berkson and Classical Measurement Error in Radon Exposure Using a Bayesian Structural Approach in the Analysis of Lung Cancer Mortality in the French Cohort of Uranium Miners

International audienceMany occupational cohort studies on underground miners have demonstrated that radon exposure is associated with an increased risk of lung cancer mortality. However, despite the deleterious consequences of exposure measurement error on statistical inference, these analyses traditionally do not account for exposure uncertainty. This might be due to the challenging nature of measurement error resulting from imperfect surrogate measures of radon exposure. Indeed, we are typically faced with exposure uncertainty in a time-varying exposure variable where both the type and the magnitude of error may depend on period of exposure. To address the challenge of accounting for multiplicative and heteroscedastic measurement error that may be of Berkson or classical nature, depending on the year of exposure, we opted for a Bayesian structural approach, which is arguably the most flexible method to account for uncertainty in exposure assessment. We assessed the association between occupational radon exposure and lung cancer mortality in the French cohort of uranium miners and found the impact of uncorrelated multiplicative measurement error to be of marginal importance. However, our findings indicate that the retrospective nature of exposure assessment that occurred in the earliest years of mining of this cohort as well as many other cohorts of underground miners might lead to an attenuation of the exposure-risk relationship. More research is needed to address further uncertainties in the calculation of lung dose, since this step will likely introduce important sources of shared uncertainty. © 2017 by Radiation Research Society

Effect of time-dependent transitions for dynamics of structured populations in epidemiological models

International audienceIn structured livestock populations like cattle herds, movements between groups of animals often depend on age or physiological status of animals, thus on the time spent in a group. In epidemiological models, transitions between groups are usually assumed Markovian (independent on the time spent in the group). Our objective was to study the effect of a transition between groups under a triangular distribution of the transition time on the pathogen spread within a structured livestock population, compared to the effect of a Markovian transition under an exponential distribution. A SIR stochastic model was defined for a population separated into youngstock and adults. Due to the separation into groups, an heterogeneity of the pathogen transmission was assumed: direct transmission within each group and indirect transmission between groups The pathogen spread after an introduction of a newborn infected animal was simulated both with an exponential (Markov) and a triangular distribution for the youngstock to adults transition time. These two distributions may lead to significant differences in the epidemic size both in the youngstock group and in the adult group. But, at the population level, the epidemic sizes were not significantly different. The persistence of the pathogen was not significantly different

Influence de la structuration du troupeau en lots sur la propagation du virus de la Diarrhée Virale Bovine (BVDV) en élevage bovin laitier

National audienceThe spread of the Bovine Viral Diarrhoea Virus (BVDV) within a herd is influenced by the herd management. In dairy herds, animals are distributed into subgroups and this induces an heterogeneity in the probability of BVDV transmission. This paper presents a study of the influence of the level of separation between subgroups on the BVDV spread. A simulation model of the BVDV spread was used. In a totally susceptible herd, a freshening non-PI heifer with a PI fetus was introduced. The BVDV spread was simulated assuming different levels of contacts between subgroups (no contact, intermediate or high level of contacts). The influence of this level of contacts between subgroups on the probability of BVDV transmission was considered by defining different BVDV transmission rates. In the results, the BVDV persistence, the extent of infection and the variability of the dynamics of the BVDV spread were found to be influenced by the level of contacts between subgroups. Therefore, the separation of animals into subgroups has to be taken into account to model the BVDV spread in order to assess the control programsLa propagation du virus de la Diarrhée Virale Bovine (BVDV) dans un troupeau est influencée par la structure du troupeau. Dans les troupeaux laitiers en particulier, les animaux sont conduits en lots plus ou moins fortement séparés. Ceci induit une hétérogénéité de la probabilité de transmission du virus et pourrait conduire à accroître la variabilité des dynamiques de propagation. L’objectif de cette étude est d’évaluer l’influence du degré de séparation entre lots sur les dynamiques de propagation du virus BVDV. Un modèle de simulation de la propagation du virus BVDV a été utilisé. Dans un troupeau sans exposition antérieure au virus, une génisse non Infectée Permanente Immunotolérante (IPI) portant un fœtus IPI a été introduite. Différents niveaux de contacts entre lots (absence, intermédiaire, élevé) ont été simulés. L’effet du degré de séparation entre lots sur la transmission horizontale du virus a été pris en compte par des taux de transmission différents. Dans les résultats, la persistance du virus, l’étendue de l’infection et la variabilité des dynamiques de propagation sont influencées par le degré de séparation. La structuration en lots et le degré de séparation doivent donc être pris en compte pour la modélisation de la propagation du virus BVDV en vue de l’élaboration de plans de maîtris

Effect of time-dependent transitions for dynamics of structured populations in epidemiological models

International audienceIn structured livestock populations like cattle herds, movements between groups of animals often depend on age or physiological status of animals, thus on the time spent in a group. In epidemiological models, transitions between groups are usually assumed Markovian (independent on the time spent in the group). Our objective was to study the effect of a transition between groups under a triangular distribution of the transition time on the pathogen spread within a structured livestock population, compared to the effect of a Markovian transition under an exponential distribution. A SIR stochastic model was defined for a population separated into youngstock and adults. Due to the separation into groups, an heterogeneity of the pathogen transmission was assumed: direct transmission within each group and indirect transmission between groups The pathogen spread after an introduction of a newborn infected animal was simulated both with an exponential (Markov) and a triangular distribution for the youngstock to adults transition time. These two distributions may lead to significant differences in the epidemic size both in the youngstock group and in the adult group. But, at the population level, the epidemic sizes were not significantly different. The persistence of the pathogen was not significantly different

Dynamics of the spread of the Bovine Viral Diarrhoea Virus (BVDV) within a dairy herd according to the separation of animals into subgroups

International audienc

Influence de la structuration du troupeau en lots sur la propagation du virus de la Diarrhée Virale Bovine (BVDV) en élevage bovin laitier

National audienceThe spread of the Bovine Viral Diarrhoea Virus (BVDV) within a herd is influenced by the herd management. In dairy herds, animals are distributed into subgroups and this induces an heterogeneity in the probability of BVDV transmission. This paper presents a study of the influence of the level of separation between subgroups on the BVDV spread. A simulation model of the BVDV spread was used. In a totally susceptible herd, a freshening non-PI heifer with a PI fetus was introduced. The BVDV spread was simulated assuming different levels of contacts between subgroups (no contact, intermediate or high level of contacts). The influence of this level of contacts between subgroups on the probability of BVDV transmission was considered by defining different BVDV transmission rates. In the results, the BVDV persistence, the extent of infection and the variability of the dynamics of the BVDV spread were found to be influenced by the level of contacts between subgroups. Therefore, the separation of animals into subgroups has to be taken into account to model the BVDV spread in order to assess the control programsLa propagation du virus de la Diarrhée Virale Bovine (BVDV) dans un troupeau est influencée par la structure du troupeau. Dans les troupeaux laitiers en particulier, les animaux sont conduits en lots plus ou moins fortement séparés. Ceci induit une hétérogénéité de la probabilité de transmission du virus et pourrait conduire à accroître la variabilité des dynamiques de propagation. L’objectif de cette étude est d’évaluer l’influence du degré de séparation entre lots sur les dynamiques de propagation du virus BVDV. Un modèle de simulation de la propagation du virus BVDV a été utilisé. Dans un troupeau sans exposition antérieure au virus, une génisse non Infectée Permanente Immunotolérante (IPI) portant un fœtus IPI a été introduite. Différents niveaux de contacts entre lots (absence, intermédiaire, élevé) ont été simulés. L’effet du degré de séparation entre lots sur la transmission horizontale du virus a été pris en compte par des taux de transmission différents. Dans les résultats, la persistance du virus, l’étendue de l’infection et la variabilité des dynamiques de propagation sont influencées par le degré de séparation. La structuration en lots et le degré de séparation doivent donc être pris en compte pour la modélisation de la propagation du virus BVDV en vue de l’élaboration de plans de maîtris