Sophie Ancelet

Sophie Ancelet
Institut de Radioprotection et de Sûreté Nucléaire (IRSN) | IRSN · LEPID - Laboratory of Epidemiology

PhD

About

41
Publications
4,026
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
692
Citations
Citations since 2016
14 Research Items
380 Citations
20162017201820192020202120220204060
20162017201820192020202120220204060
20162017201820192020202120220204060
20162017201820192020202120220204060
Additional affiliations
October 2011 - present
Institut de Radioprotection et de Sûreté Nucléaire (IRSN)
Position
  • Researcher
March 2011 - September 2011
Électricité de France (EDF)
Position
  • PostDoc Position
December 2009 - March 2011
French National Institute for Agriculture, Food, and Environment (INRAE)
Position
  • PostDoc Position
Education
November 2004 - July 2008
AgroParisTech - Department of Applied Mathematics and Informatics
Field of study
  • Biostatistics

Publications

Publications (41)
Article
Objectives: Increased risks of central nervous system (CNS) tumors and leukemia associated with computed tomography (CT) exposure during childhood have been reported in recent epidemiological studies. However, no evidence of increased risks was suggested in a previous analysis of the French CT cohort. This study benefits from an updated cohort wit...
Article
Full-text available
As multifactorial and chronic diseases, cancers are among these pathologies for which the exposome concept is essential to gain more insight into the associated etiology and, ultimately, lead to better primary prevention strategies for public health. Indeed, cancers result from the combined influence of many genetic, environmental and behavioral st...
Article
Full-text available
Epidemiological data on cohorts of occupationally exposed uranium miners are currently used to assess health risks associated with chronic exposure to low doses of ionizing radiation. Nevertheless, exposure uncertainty is ubiquitous and questions the validity of statistical inference in these cohorts. This paper highlights the flexibility and relev...
Article
Full-text available
A retrospective statistical study has been performed in order to identify the biological, clinical and behavioural variables that could potentially predict the survival status of irradiated non-human primates (NHP) and to refine the future use of humane endpoints (HEP). The available data come from experiments that were initially designed and imple...
Article
Nowadays, protection standards against ionizing radiation health effects are mainly derived from the results of the epidemiological study of atomic bomb survivors in Hiroshima and Nagasaki, who were exposed externally to radiation, at high dose rate. However, exposure to ionizing radiation in the general population and in workers generally occurs a...
Article
Exposure 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 me...
Article
Full-text available
Exposure measurement error represents one of the most important sources of uncertainty in epidemiology. When exposure uncertainty is not or only poorly accounted for, it can lead to biased risk estimates and a distortion of the shape of the exposure-response relationship. In occupational cohort studies, the time-dependent nature of exposure and cha...
Article
Many 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 t...
Article
Accounting for uncertainty in exposure assessment in occupational cohort studies can be daunting, since we are typically faced with time-varying exposure and both type and magnitude of measurement error may depend on period of exposure. On the other hand, ignoring exposure uncertainty may lead to biassed effect estimates, a distortion in the exposu...
Article
Full-text available
The potential health impacts of chronic exposures to uranium, as they occur in occupational settings, are not well characterized. Most epidemiological studies have been limited by small sample sizes, and a lack of harmonization of methods used to quantify radiation doses resulting from uranium exposure. Experimental studies have shown that uranium...
Article
The objectives are to analyze mortality risks in the extended follow-up of the French uranium miners' cohort and to examine their potential relation to occupational exposure to ionizing radiation (IR). The total cohort includes 5,086 uranium miners employed in the CEA-COGEMA group and followed up from 1946 to 2007. Vital status, causes of death, an...
Article
Full-text available
The investigation of potential adverse health effects of occupational exposures to ionizing radiation, on uranium miners, is an important area of research. Radon is a well-known carcinogen for lung, but the link between radiation exposure and other diseases remains controversial, particularly for kidney cancer. The aims of this study were therefore...
Article
Ecological data such as biomasses often present a high proportion of zeros with possible skewed positive values. The Delta-Gamma (DG) approach, which models separately the presence–absence and the positive biomass, is commonly used in ecology. A less commonly known alternative is the compound Poisson-gamma (CPG) approach, which essentially mimics t...
Article
Full-text available
The potential adverse effects associated with exposure to ionizing radiation from computed tomography (CT) in pediatrics must be characterized in relation to their expected clinical benefits. Additional epidemiological data are, however, still awaited for providing a lifelong overview of potential cancer risks. This paper gives predictions of poten...
Article
Introduction Nested case-control studies are classically used in epidemiology to reduce time and cost for data collection while minimising bias induced by sample size reduction. However, if the continuous exposure of interest have a highly skewed probability distribution, rare exposure values are less likely to be selected. Therefore, the sampling...
Article
Full-text available
Previous epidemiological studies and quantitative risk assessments (QRA) have suggested that natural background radiation may be a cause of childhood leukemia. The present work uses a QRA approach to predict the excess risk of childhood leukemia in France related to three components of natural radiation: radon, cosmic rays and terrestrial gamma ray...
Article
The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts' knowledge a...
Article
Full-text available
Background As discontinuation in in vitro fertilization (IVF) programs has been associated with a poor prognosis, one hypothesis is that some couple-specific predictive factors in IVF may be shared with opposite effect by both success (i.e. live birth) and treatment discontinuation processes. Our objective was to perform a joint analysis of these t...
Data
The appendix includes detailed information on the shared random effects model.
Article
When analyzing the geographical variations of disease risk, one common problem is data sparseness. In such a setting, we investigate the possibility of using Bayesian shared spatial component models to strengthen inference and correct for any spatially structured sources of bias, when distinct data sources on one or more related diseases are availa...
Article
We describe the application of Bayesian hierarchical models (BHM) to the analysis of risk of sheep scrapie using data from multiple surveillance sources. More specifically, we analysed data from the test results of three surveillance sources on classical and atypical scrapie in Wales for the period 2002-2006. For each form of scrapie, a BHM was fit...
Article
A parsimonious model is presented as an alternative to delta approaches to modelling zero-inflated continuous data. The data model relies on an exponentially compound Poisson process, also called the law of leaks (LOL). It represents the process of sampling resources that are spatially distributed as Poisson distributed patches, each containing a c...
Conference Paper
Full-text available
Les mécanismes de censure dits informatifs viennent souvent complexifier l'analyse de données de survie. L'inférence de modèles standards ne tenant pas compte de ce type de données manquantes peut mener à des conclusions biaisées. De nombreux travaux se sont axés autour des modèles à effets aléatoires partagés pour l'analyse de données longitudinal...
Article
Full-text available
Ces dernières années, la modélisation hiérarchique bayésienne a connu un essor considérable en épidémiologie géographique. Les principaux modèles développés sont axés autour de la description spatiale et spatio-temporelle des variations du risque d'une ou plusieurs maladies à partir d'une source de recensement unique des cas. Parallèlement, l'analy...
Article
Full-text available
For most ecological questions, the random processes studied are spatially structured and come from the combined effect of several observed or unobserved random variables interacting at various scales. In practice, when data can't be directly treated with traditional spatial structures, observations are often considered as independent. Moreover, the...
Article
Full-text available
Spatial Bayesian clustering algorithms can provide correct inference of population genetic structure when applied to populations for which continuous variation of allele frequencies is disrupted by small discontinuities. Here we review works which used Bayesian clustering algorithms for studying the Scandinavian brown bears, with particular attenti...
Article
Full-text available
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 th...
Article
Full-text available
BACKGROUND Early detection of new diseases such as bovine spongiform encephalopathy is the subject of great interest (Gibbens et al., 2008). Understanding whether a disease is infectious or sporadic becomes essential for the application of control measures. Consistent and robust ways to the assessment of temporal trends are required to help in the...
Article
For most ecological questions, the random processes studied are spatially structured and come from the combined effect of several observed or unobserved random variables interacting at various scales. In practice, when data can't be directly treated with traditional spatial structures, observations are often considered as independent. Moreover, the...

Network

Cited By

Projects

Project (1)
Project
RADIO-AIDE is a multidisciplinary project that aims to develop advanced spatio-temporal models and new Artifical Intelligence tools for brain Magnetic Resonance Imaging (MRI) data processing to : a) generate new knowledge about the underlying neurotoxic mechanisms implied in the initiation and temporal progression of cognitive dysfunctions following brain radiotherapy (RT) and the radioresistance of targeted brain structures, accounting for the tumor-response status as contextual data; b) predict individual cognitive impairment at early stage after brain RT to set up mitigation measures to preserve the quality of life for survivors; c) provide to clinicians a usable academic tool to perform an automated processing of MRI data acquired in clinical routine, from the longitudinal extraction of clinically relevant image-based biomarkers. The project will be guided by the rich and multimodal data from the EpiBrainRad cohort including patients treated by RT for a high grade glioma.