Shapley effects and proportional marginal effects for global sensitivity analysis: application to computed tomography scan organ dose estimation

Concerns have been raised about possible cancer risks after exposure to computed tomography (CT) scans in childhood. The health effects of ionizing radiation are then estimated from the absorbed dose to the organs of interest which is calculated, for each CT scan, from dosimetric numerical models, like the one proposed in the NCICT software. Given that a dosimetric model depends on input parameters which are most often uncertain, the calculation of absorbed doses is inherently uncertain. A current methodological challenge in radiation epidemiology is thus to be able to account for dose uncertainty in risk estimation. A preliminary important step can be to identify the most influential input parameters implied in dose estimation, before modelling and accounting for their related uncertainty in radiation-induced health risks estimates. In this work, a variance-based global sensitivity analysis was performed to rank by influence the uncertain input parameters of the NCICT software implied in brain and red bone marrow doses estimation, for four classes of CT examinations. Two recent sensitivity indices, especially adapted to the case of dependent input parameters, were estimated, namely: the Shapley effects and the Proportional Marginal Effects (PME). This provides a first comparison of the respective behavior and usefulness of these two indices on a real medical application case. The conclusion is that Shapley effects and PME are intrinsically different, but complementary. Interestingly, we also observed that the proportional redistribution property of the PME allowed for a clearer importance hierarchy between the input parameters

Exploiter l'approche hiérarchique bayésienne pour la modélisation statistique de structures spatiales. Application en écologie des populations

Diplôme : Dr. Ing.Dans la plupart des questions écologiques, les phénomènes aléatoires d'intérêt sont spatialement structurés et issus de l'effet combiné de multiples variables aléatoires, observées ou non, et inter-agissant à diverses échelles. En pratique, dès lors que les données de terrain ne peuvent être directement traitées avec des structures spatiales standards, les observations sont généralement considérées indépendantes. Par ailleurs, les modèles utilisés sont souvent basés sur des hypothèses simplificatrices trop fortes par rapport à la complexité des phénomènes étudiés. Dans ce travail, la démarche de modélisation hiérarchique est combinée à certains outils de la statistique spatiale afin de construire des structures aléatoires fonctionnelles "sur-mesure" permettant de représenter des phénomènes spatiaux complexes en écologie des populations. L'inférence de ces différents modèles est menée dans le cadre bayésien avec des algorithmes MCMC. Dans un premier temps, un modèle hiérarchique spatial (Geneclust) est développé pour identifier des populations génétiquement homogènes quand la diversité génétique varie continûment dans l'espace. Un champ de Markov caché, qui modélise la structure spatiale de la diversité génétique, est couplé à un modèle bivarié d'occurrence de génotypes permettant de tenir compte de l'existence d'unions consanguines chez certaines populations naturelles. Dans un deuxième temps, un processus de Poisson composé particulier, appelé loi des fuites, est présent e sous l'angle de vue hiérarchique pour décrire le processus d'échantillonnage d'organismes vivants. Il permet de traiter le délicat problème de données continues présentant une forte proportion de zéros et issues d'échantillonnages à efforts variables. Ce modèle est également couplé à différents modèles sur grille (spatiaux, régionalisés) afin d'introduire des dépendances spatiales entre unités géographiques voisines puis, à un champ géostatistique bivarié construit par convolution sur grille discrète afin de modéliser la répartition spatiale conjointe de deux espèces. Les capacités d'ajustement et de prédiction des différents modèles hiérarchiques proposés sont comparées aux modèles traditionnellement utilisés à partir de simulations et de jeux de données réelles (ours bruns de Suède, invertébrés épibenthiques du Golfe-du-Saint-Laurent (Canada))

Estimation of lung cancer risk associated to multiple correlated sources of ionizing radiation in the post-55 French cohort of uranium miners

International audienceIn radiation epidemiology, the health effects of occupational exposures are often studied separately for each source of ionizing radiation (IR). Radiation protection standards are thus mainly based on risk analyses focusing on a single exposure. But, nuclear workers are exposed to several sources of correlated exposures such as IR, chemical and physical agents. In this work, we focus on the risk of death by lung cancer in the post-55 French cohort of uranium miners who are chronically exposed to low doses of radon, gamma rays and uranium dust. We propose a Bayesian hierarchical approach called "Bayesian Profile Regression". This model makes it possible to estimate the radiation-related risk of death by lung cancer in the specific context of multiple and correlated exposures to IR, for which simple multiple regressions may lead to unstable and unprecise risk estimates. It is based on the combination of three conditionally independent sub-models: a survival disease sub-model, a classification sub-model and an exposure sub-model. Fitting this model under the Bayesian paradigm allows clustering individuals with similar profiles, that is, with similar exposure characteristics, and estimating the associated excess hazard risk (EHR) of radiation-related death by lung cancer for each group, in a unique step. Finally, the obtained results are post-processed to identify and characterize profiles of uranium miners with high or low EHR. Our model distinguished eight different profiles of uranium miners corresponding to eight different EHR. Among them, two profiles were associated with a strictly positive and statistically significant EHR. The first group (EHR=1.4, 95%IC=[0.60, 2.60]) corresponded to the miners the most highly exposed to radon, gamma rays and uranium dust and for more than 19 years (mainly before mechanization). The second group (EHR=1.2, 95%IC=[0.17, 2.80]) corresponded to miners who were very young when first exposed and exposed at high doses to radon, gamma rays and uranium dust (mainly after mechanization). Finally, the model showed that the group of miners who worked mainly in the Hérault mine- the only included uranium mine with sedimentary soil- had lower EHR. These results do not account for the smoking status of miners for whom information is strongly missing in the cohort. Our short term perspectives are thus to try to account for the smoking status in our Bayesian profile regression but also on exposure measurement errors on radon, gamma rays and uranium dust

Bayesian hierarchical modeling to account for complex patterns of measurement error in cohort studies. Application in radiation epidemiology.

International audienceDespite its deleterious consequences for statistical inference and its ubiquity in observationalresearch, exposure measurement error is rarely accounted for in epidemiological studies. Basically,standard correction methods, like regression calibration or SIMEX, often lack the flexibility to accountfor complex patterns of exposure uncertainty. However, in occupational cohort studies, for instance,changes in the methods of exposure assessment can lead to complex error structures. Moreover, astrategy of group-exposure assessment and individual worker characteristics may lead to errorcomponents that are shared between or within individuals. In this work, we thus propose and fit severalBayesian hierarchical models, combining survival models with time-dependent covariates,measurement and exposure models, to obtain a corrected estimate ofthe potential association betweenexposure to radon and mortality for several cancer types in the French cohort of uranium miners. Asimulation study is under progress to assess the impact of model misspecification on risk estimates

Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics

We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it can check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set

A Bayesian hierarchical approach to account for shared exposuremeasurement error in an occupational cohort

International audienceExposure measurement error poses one of the most important threats to the validity of statistical inference in epidemiological studies. Indeed, when exposure measurement is not or only poorly accounted for, it may lead to biased risk estimates, loss in statistical power and distortions of the exposure-response relationship. Despite these deleterious consequences and despite its ubiquity in observational research, exposure measurement is rarely accounted for in the estimation of risk coefficients in epidemiological studies. This may partly be due to the fact that classical methods that are routinely used to correct for measurement error, like simulation extrapolation or regression calibration, lack the flexibility to account for complex patterns of exposure uncertainty. In occupational cohort studies, for instance, changes in the methods of exposure assessment can lead to complex structures of exposure measurement error. Moreover, the use of a strategy of group-exposure assessment, for instance via job exposure matrices, and individual worker characteristics may lead to error components that are shared between or within workers, respectively. We propose several hierarchical models and conduct Bayesian inference for these models to obtain corrected risk estimates on the association between exposure to radon and its decay products and lung cancer mortality in the French cohort of uranium miners. The hierarchical approach, which is based on the combination of sub-models that are linked via conditional independence assumptions, provides a flexible and coherent framework for the modelling of complex error structures.We observe a marked increase in the excess hazard ratio when accounting for shared measurement error in a proportional hazards model whereas the correction for unshared measurement error is only of marginal importance in risk estimation. These results, which are in accordance with previous results on the impact of different measurement error characteristics in the French cohort of uranium miners obtained on simulated data, underline the importance of a careful characterization of all components of exposure measurement error in an occupational cohort study. In this context, the use of a Bayesian hierarchical approach provides the possibility to integrate expert knowledge or to combine epidemiological data with experimental laboratory data in a coherent framework

Modélisation hiérarchique bayésienne pour la prise en compte d’erreurs de mesure partagées dans les études de cohorte. Application en épidémiologie des rayonnements ionisants.

International audienceDans les études de cohorte, on s’intéresse généralement à l’association entre le temps jusqu’au décès par une certaine pathologie et l’exposition cumulée à un (ou plusieurs) agent(s) pathogène(s). Dans ce contexte, l’historique d’exposition des individus est généralement estimé rétrospectivement ou mesuré de manière prospective en utilisant différentes stratégies selon la période d’exposition. Cela peut créer des combinaisons d’erreurs de mesure assez complexes, notamment caractérisées par une hétérogéneité dans le temps du type et de la magnitude des erreurs. Par ailleurs, si une erreur est commise sur l’estimation d’un niveau d’exposition supposé commun à un groupe d’individus, cela peut créer des erreurs de mesure dites partagées entre individus. En outre, les conditions d’expositions et les pratiques individuelles peuvent créer des erreurs partagées intra-individus. Bien qu’il soit difficile de prendre en compte des combinaisons d’erreurs de mesure partagées et hétéroscédastiques avec des approches statistiques standard, celles-ci peuvent affecter de façon significative (i.e., biais, perte de puissance, atténuation de la courbe exposition risque) les inférences statistiques menées dans le cadre d’études épidémiologiques. Dans ce travail, nous avons proposé deux structures hiérarchiques possibles ainsi que des algorithmes Metropolis-Within-Gibbs adaptatifs spécifiques permettant de tenir compte de l’existence d’erreurs de mesure partagées dans un modèle de survie en excès de risque instantané. Ce travail a été motivé par un cas d’étude de cohorte professionnelle en épidémiologie des rayonnements ionisants. L’objectif est d’estimer le risque de décès par cancer du poumon - corrigé des erreurs de mesure partagées sur l’exposition au radon - dans la cohorte française des mineurs d’uranium. Une nette augmentation de l’excès de risque instantané de décès par cancer du poumon a été observée par rapport à une estimation sans prise en compte d’erreurs de mesure ou avec seulement prise en compte d’erreurs de mesure non partagées. Une étude par simulations est actuellement en cours afin d’analyser l’impact d’une mauvaise spécification de modèles sur l’estimation du risque

Estimation of lung cancer risk associated to multiple correlated sources of ionizing radiation in the post-55 French cohort of uranium miners

International audienceIn radiation epidemiology, the health effects of occupational exposures are often studied separately for each source of ionizing radiation (IR). Radiation protection standards are thus mainly based on risk analyses focusing on a single exposure. But, nuclear workers are exposed to several sources of correlated exposures such as IR, chemical and physical agents. In this work, we focus on the risk of death by lung cancer in the post-55 French cohort of uranium miners who are chronically exposed to low doses of radon, gamma rays and uranium dust. We propose a Bayesian hierarchical approach called "Bayesian Profile Regression". This model makes it possible to estimate the radiation-related risk of death by lung cancer in the specific context of multiple and correlated exposures to IR, for which simple multiple regressions may lead to unstable and unprecise risk estimates. It is based on the combination of three conditionally independent sub-models: a survival disease sub-model, a classification sub-model and an exposure sub-model. Fitting this model under the Bayesian paradigm allows clustering individuals with similar profiles, that is, with similar exposure characteristics, and estimating the associated excess hazard risk (EHR) of radiation-related death by lung cancer for each group, in a unique step. Finally, the obtained results are post-processed to identify and characterize profiles of uranium miners with high or low EHR. Our model distinguished eight different profiles of uranium miners corresponding to eight different EHR. Among them, two profiles were associated with a strictly positive and statistically significant EHR. The first group (EHR=1.4, 95%IC=[0.60, 2.60]) corresponded to the miners the most highly exposed to radon, gamma rays and uranium dust and for more than 19 years (mainly before mechanization). The second group (EHR=1.2, 95%IC=[0.17, 2.80]) corresponded to miners who were very young when first exposed and exposed at high doses to radon, gamma rays and uranium dust (mainly after mechanization). Finally, the model showed that the group of miners who worked mainly in the Hérault mine- the only included uranium mine with sedimentary soil- had lower EHR. These results do not account for the smoking status of miners for whom information is strongly missing in the cohort. Our short term perspectives are thus to try to account for the smoking status in our Bayesian profile regression but also on exposure measurement errors on radon, gamma rays and uranium dust

Estimation of lung cancer risk associated to multiple correlated sources of ionizing radiation in the post-55 French cohort of uranium miners

International audienceIn radiation epidemiology, the health effects of occupational exposures are often studied separately for each source of ionizing radiation (IR). Radiation protection standards are thus mainly based on risk analyses focusing on a single exposure. But, nuclear workers are exposed to several sources of correlated exposures such as IR, chemical and physical agents. In this work, we focus on the risk of death by lung cancer in the post-55 French cohort of uranium miners who are chronically exposed to low doses of radon, gamma rays and uranium dust. We propose a Bayesian hierarchical approach called "Bayesian Profile Regression". This model makes it possible to estimate the radiation-related risk of death by lung cancer in the specific context of multiple and correlated exposures to IR, for which simple multiple regressions may lead to unstable and unprecise risk estimates. It is based on the combination of three conditionally independent sub-models: a survival disease sub-model, a classification sub-model and an exposure sub-model. Fitting this model under the Bayesian paradigm allows clustering individuals with similar profiles, that is, with similar exposure characteristics, and estimating the associated excess hazard risk (EHR) of radiation-related death by lung cancer for each group, in a unique step. Finally, the obtained results are post-processed to identify and characterize profiles of uranium miners with high or low EHR. Our model distinguished eight different profiles of uranium miners corresponding to eight different EHR. Among them, two profiles were associated with a strictly positive and statistically significant EHR. The first group (EHR=1.4, 95%IC=[0.60, 2.60]) corresponded to the miners the most highly exposed to radon, gamma rays and uranium dust and for more than 19 years (mainly before mechanization). The second group (EHR=1.2, 95%IC=[0.17, 2.80]) corresponded to miners who were very young when first exposed and exposed at high doses to radon, gamma rays and uranium dust (mainly after mechanization). Finally, the model showed that the group of miners who worked mainly in the Hérault mine- the only included uranium mine with sedimentary soil- had lower EHR. These results do not account for the smoking status of miners for whom information is strongly missing in the cohort. Our short term perspectives are thus to try to account for the smoking status in our Bayesian profile regression but also on exposure measurement errors on radon, gamma rays and uranium dust