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380 results on '"Liquet, Benoit"'

101. Sparse partial least squares with group and subgroup structure

Author

Sutton, M., Thiébaut, R., Liquet, Benoit, Queensland University of Technology [Brisbane] (QUT), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
surgical procedures, operative, education, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [MATH]Mathematics [math], musculoskeletal system, human activities, health care economics and organizations, SISTM
Abstract

International audience; Integrative analysis of high dimensional omics datasets has been studied by many authors in recent years. By incorporating prior known relationships among the variables, these analyses have been successful in elucidating the relationships between different sets of omics data. In this article, our goal is to identify important relationships between genomic expression and cytokine data from a human immunodeficiency virus vaccine trial. We proposed a flexible partial least squares technique, which incorporates group and subgroup structure in the modelling process. Our new method accounts for both grouping of genetic markers (eg, gene sets) and temporal effects. The method generalises existing sparse modelling techniques in the partial least squares methodology and establishes theoretical connections to variable selection methods for supervised and unsupervised problems. Simulation studies are performed to investigate the performance of our methods over alternative sparse approaches. Our R package sgspls is available at https://github.com/matt-sutton/sgspls.

Published
2018

102. Pre-diagnostic blood immune markers, incidence and progression of B-cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses

Author

Vermeulen, Roel, Hosnijeh, Fatemeh Saberi, Bodinier, Barbara, Portengen, Lutzen, Liquet, Benoit, Garrido-Manriquez, Javiera, Lokhorst, Henk, Bergdahl, Ingvar A., Kyrtopoulos, Soterios A., Johansson, Ann-Sofie, Georgiadis, Panagiotis, Melin, Beatrice, Palli, Domenico, Krogh, Vittorio, Panico, Salvatore, Sacerdote, Carlotta, Tumino, Rosario, Vineis, Paolo, Castagne, Raphaele, Chadeau-Hyam, Marc, Botsivali, Maria, Chatziioannou, Aristotelis, Valavanis, Ioannis, Kleinjans, Jos C. S., de Kok, Theo M. C. M., Keun, Hector C., Athersuch, Toby J., Kelly, Rachel, Lenner, Per, Hallmans, Goran, Stephanou, Euripides G., Myridakis, Antonis, Kogevinas, Manolis, Fazzo, Lucia, De Santis, Marco, Comba, Pietro, Bendinelli, Benedetta, Kiviranta, Hannu, Rantakokko, Panu, Airaksinen, Riikka, Ruokojarvi, Paivi, Gilthorpe, Mark, Fleming, Sarah, Fleming, Thomas, Tu, Yu-Kang, Lundh, Thomas, Chien, Kuo-Liong, Chen, Wei J., Lee, Wen-Chung, Hsiao, Chuhsing Kate, Aucun, Stichting WBBS, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), Unit of Molecular and Nutritional Epidemiology, Institute for Cancer Research and Prevention, Department of Preventive & Predictive Medicine, Italian National Center Institute, Unit of Cancer Epidemiology, AOU S. Giovanni Battista, CPO Piemonte, CeRMS, University of Torino, Civile - M.P.Arezzo Hospital, Department of Epidemiology and Public Health, Imperial College London, Centre for Environment and Life Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Department of Radiation Sciences, Oncology, Umeå University, Department of Public Health and Clinical Medicine, Nutritional Research, Center for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra [Barcelona] (UPF)-Catalunya ministerio de salud, RS: GROW - R1 - Prevention, RS: MHeNs - R3 - Neuroscience, Toxicogenomics, RS: FSE MaCSBio, RS: FPN MaCSBio, and RS: FHML MaCSBio

Subjects
mixed-effect modeling, BONE-MARROW, prospective cohort, FACTOR-ALPHA, lymphoma, multivariate models, SERUM-LEVELS, time to diagnosis, OVARIAN-CANCER, TRANSFORMING-GROWTH-FACTOR, multiple myeloma, CYTOKINE LEVELS, POOR-PROGNOSIS, cytokine, INDEPENDENT PREDICTOR, [MATH]Mathematics [math], NON-HODGKIN-LYMPHOMA, SOLUBLE CD30
Abstract

ACL; International audience; Recent prospective studies have shown that dysregulation of the immune system may precede the development of B-cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case-control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01-15.97) in 268 incident cases of BCL (including multiple myeloma [ MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor-2 (FGF-2 p = 7.2 x 10(-4)) and transforming growth factor alpha (TGF-alpha, p = 6.5 x 10(-5)) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF-2 (p = 7.8 x 10(-7)), TGF-alpha (p = 4.08 x 10(-5)), fractalkine (p = 1.12 x 10(-3)), monocyte chemotactic protein-3 (p = 1.36 x 10(-4)), macrophage inflammatory protein 1-alpha (p = 4.6 x 10(-4)) and vascular endothelial growth factor (p = 4.23 x 10(-5)). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case-only analyses showed that Granulocyte-macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth-factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.What's new?B-cell lymphomas (BCL) are frequent in immunocompromised individuals, but most BCL cases are thought to occur as a consequence of minor immune perturbations in otherwise immunocompetent individuals. Here the authors prospectively examined a panel of immune markers in the blood from 268 patients afflicted with BCL and paired controls. The data uncover a functional role for growth factors (i.e. FGF-2, TGF-alpha) in the incidence and progression of multiple myeloma, a BCL subtype, and underscore the importance of chemokine and cytokine regulation in diffuse large B-cell lymphoma and chronic lymphocytic leukemia.

Published
2018

103. Investigating Gene- and Pathway-environment Interaction analysis approaches

Author

Broc, C., Evangelou, M., Guenel, P., Truong, T., Liquet, Benoit, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
surgical procedures, operative, education, [MATH]Mathematics [math], musculoskeletal system, human activities, health care economics and organizations
Abstract

ACL; International audience

Published
2018

105. Sparse partial least squares with group and subgroup structure

Author

Sutton, Matt, Thiebaut, Rodolphe, Liquet, Benoit, Sutton, Matt, Thiebaut, Rodolphe, and Liquet, Benoit

Abstract

Integrative analysis of high dimensional omics datasets has been studied by many authors in recent years. By incorporating prior known relationships among the variables, these analyses have been successful in elucidating the relationships between different sets of omics data. In this article, our goal is to identify important relationships between genomic expression and cytokine data from a human immunodeficiency virus vaccine trial. We proposed a flexible partial least squares technique, which incorporates group and subgroup structure in the modelling process. Our new method accounts for both grouping of genetic markers (eg, gene sets) and temporal effects. The method generalises existing sparse modelling techniques in the partial least squares methodology and establishes theoretical connections to variable selection methods for supervised and unsupervised problems. Simulation studies are performed to investigate the performance of our methods over alternative sparse approaches.

Published
2018

106. Investigating gene- and pathway-environment interaction analysis approaches

Author

Broc, Camilo, Evangelou, Marina, Guenel, Pascal, Truong, Therese, Liquet, Benoit, Broc, Camilo, Evangelou, Marina, Guenel, Pascal, Truong, Therese, and Liquet, Benoit

Abstract

Free to read on publisher website Pathway analysis can increase power to detect associations with a gene or a pathway by combining several signals at the single nucleotide polymorphism (SNP)-level into a single test. In this work, we propose to extend two well-known self-contained methods, the Fisher’s method (FM) and the Adaptive Rank Truncated Product (ARTP) method to the analysis of gene-environment (GxE) interaction at the gene and pathway-level. It has been previously suggested that the permutation procedures that are usually used to derive the significance of these tests are not appropriate for the analysis of GxE interaction and should be replaced by a bootstrap approach. We analyse and compare the performance of the extension of FM and ARTP using the permutation and the parametric bootstrap procedure in simulation studies. We illustrate its application by analysing the interaction between night work and circadian gene polymorphisms in the risk of breast cancer in a case-control study. The ARTP method, adapted for both gene- and pathway-environment interactions, gives promising results and has been wrapped to the R package PIGE available on the CRAN.

Published
2018

107. CEoptim: Cross-Entropy R Package for Optimization

Author

Liquet, Benoit

Abstract

The cross-entropy (CE) method is a simple and versatile technique for optimization, based on Kullback-Leibler (or cross-entropy) minimization. The method can be applied to a wide range of optimization tasks, including continuous, discrete, mixed and constrained optimization problems. The new package CEoptim provides the R implementation of the CE method for optimization. We describe the general CE methodology for optimization and well as some useful modifications. The usage and efficacy of CEoptim is demonstrated through a variety of optimization examples, including model fitting, combinatorial opti- mization, and maximum likelihood estimation.

Published
2017

108. Integrative Analysis of Immunological Data to Explore Chronic Immune T-Cell Activation in Successfully Treated HIV Patients

Author

Picat, Marie-Quitterie, Pellegrin, Isabelle, Bitard, Juliette, Wittkop, Linda, Proust-Lima, Cécile, Liquet, Benoit, Moreau, Jean-François, Bonnet, Fabrice, Blanco, Patrick, Thiébaut, Rodolphe, Service de biostatistiques et information médicale [Saint-Louis], Université Paris Diderot - Paris 7 (UPD7)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service d'immunologie et d'immunogénétique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux], Institut des Neurosciences Paris-Saclay (NeuroPSI), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), Biogéosciences [UMR 6282] [Dijon] (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, CHU Pontchaillou [Rennes], Composantes innées de la réponse immunitaire et différenciation (CIRID), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique (CNRS), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Assistance publique - Hôpitaux de Paris (AP-HP)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris]-Université Paris Diderot - Paris 7 ( UPD7 ), Institut des Neurosciences de Paris-Saclay ( Neuro-PSI ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Santé Publique, d'Epidémiologie et de Développement ( ISPED ), Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Laboratoire de Mathématiques et de leurs Applications [Pau] ( LMAP ), Université de Pau et des Pays de l'Adour ( UPPA ) -Centre National de la Recherche Scientifique ( CNRS ), Biogéosciences [Dijon] ( BGS ), Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Composantes innées de la réponse immunitaire et différenciation ( CIRID ), Université Bordeaux Segalen - Bordeaux 2-Centre National de la Recherche Scientifique ( CNRS ), Institut National de Recherche en Informatique et en Automatique ( Inria ), Statistics In System biology and Translational Medicine ( SISTM ), Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Diderot - Paris 7 (UPD7)-Groupe Hospitalier Saint Louis - Lariboisière - Fernand Widal [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Biogéosciences [UMR 6282] (BGS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), and Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)

Subjects
RNA viruses, CD4-Positive T-Lymphocytes, Male, Physiology, T-Lymphocytes, Cytomegalovirus, Autoimmunity, HIV Infections, CD8-Positive T-Lymphocytes, Pathology and Laboratory Medicine, Lymphocyte Activation, Biochemistry, Cohort Studies, White Blood Cells, Immunodeficiency Viruses, Animal Cells, Risk Factors, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Immune Physiology, RNA, Ribosomal, 16S, Medicine and Health Sciences, Enzyme-Linked Immunoassays, Immune Response, ComputingMilieux_MISCELLANEOUS, Immune System Proteins, T Cells, Middle Aged, Anti-Retroviral Agents, Medical Microbiology, Viral Pathogens, Viruses, Human Cytomegalovirus, Female, France, Cellular Types, Pathogens, Algorithms, Research Article, musculoskeletal diseases, Adult, Herpesviruses, Immune Cells, Immunology, Cytotoxic T cells, Research and Analysis Methods, Microbiology, Antibodies, Immune Activation, Retroviruses, Humans, [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST], Immunoassays, Microbial Pathogens, Blood Cells, Models, Statistical, Lentivirus, Immunity, Organisms, Biology and Life Sciences, HIV, Proteins, Interferon-alpha, Cell Biology, HLA-DR Antigens, ADP-ribosyl Cyclase 1, Cross-Sectional Studies, Chronic Disease, Multivariate Analysis, Immunologic Techniques, DNA viruses
Abstract

Objectives To unravel the complex relationships between cytomegalovirus-induced-, autoimmune-induced responses, microbial translocation and chronic immune activation (CIA) in successfully treated HIV-infected patients and to explore the mediating role of alpha-interferon in these processes. Design Cross-sectional study nested in the ANRS CO3 Aquitaine Cohort, a prospective hospital-based cohort of HIV-1-infected patients in South-Western France. Methods Patients initiated antiretroviral therapy between 2005 and 2008 and were treated with sustained virological suppression for at least two years. CIA was defined by the percentage of HLA-DR+/CD38+ among CD8+T-cells. Integrative analyses were performed using structural equation modelling (SEM). Results The main analysis was performed in 57 HLA-A*0201 positive patients, due to availability of percentages of actin-, vimentin-, lamin-specific CD8+T-cells (HLA-A2-restricted tests) to further characterize autoimmune response. Cytomegalovirus-induced response was assessed by Quantiferon and pp-65 ELISPOT. SEM revealed a direct effect of cytomegalovirus-induced response on CIA (standardized estimate βstd = 0.56, p-value = 0.0004). The effect of autoimmune-induced response on CIA was indirect through alpha-interferon pathway, assessed by expression levels of 5 alpha-interferon-stimulated genes ADAR, ISG15, IFIT1, Mx1 and OAS1 (effect of autoimmune response on alpha-interferon: βstd = 0.36, p-value = 0.0401; effect of alpha-interferon on CIA: βstd = 0.39, p-value = 0.0044). There was no direct effect of autoimmune-induced response on CIA (p-value = 0.3169). Microbial translocation as measured by 16SrDNA and sCD14 in plasma was not associated with CIA. Results were consistent in 142 patients in whom cytomegalovirus and auto-immunity responses were measured by Quantiferon and anti-nuclear antibodies, respectively. All analyses performed in HLA-A*0201 positive patients and in the overall population revealed a significant effect of IFN-α latent variable on CIA. Conclusion The role of cytomegalovirus-induced response on CIA was confirmed as well as the involvement of alpha-interferon on CIA. The indirect effect of auto-immunity response on CIA revealed through the alpha-interferon pathway requires further investigation to confirm the potential role of auto-immunity for CIA in HIV-infected patients.

Published
2017

112. Le logiciel R: Maitriser le langage; Effectuer des analyses (bio)statistiques (2nd edition) (Statistique et probabilites appliquees)

Author

de Micheaux, Pierre, Drouilhet, Remy, Liquet, Benoit, de Micheaux, Pierre, Drouilhet, Remy, and Liquet, Benoit

Abstract

Ce livre est consacré à un outil désormais incontournable pour l’analyse de données, l’élaboration de graphiques et le calcul statistique : le logiciel R. Après avoir introduit les principaux concepts permettant une utilisation sereine de cet environnement informatique (organisation des données, importation et exportation, accès à la documentation, représentations graphiques, programmation, maintenance, etc.), les auteurs de cet ouvrage détaillent l'ensemble des manipulations permettant la manipulation avec R d'un très grand nombre de méthodes et de notions statistiques : simulation de variables aléatoires, intervalles de confiance, tests d'hypothèses, valeur-p, bootstrap, régression linéaire, ANOVA (y compris répétées), et d'autres encore. Écrit avec un grand souci de pédagogie et clarté, agrémenté de nombreux exercices et travaux pratiques, ce livre accompagnera idéalement tous les utilisateurs de R- et ceci sur les environnements Windows, Macintosh ou Linux - qu'ils soient débutants ou d'un niveau avancé.

Published
2017

113. Bayesian variable selection regression of multivariate responses for group data

Author

Liquet, Benoit, Mengersen, Kerrie, Pettitt, Tony, Sutton, Matt, Liquet, Benoit, Mengersen, Kerrie, Pettitt, Tony, and Sutton, Matt

Abstract

We propose two multivariate extensions of the Bayesian group lasso for variable selection and estimation for data with high dimensional predictors and multi-dimensional response variables. The methods utilize spike and slab priors to yield solutions which are sparse at either a group level or both a group and individual feature level. The incorporation of group structure in a predictor matrix is a key factor in obtaining better estimators and identifying associations between multiple responses and predictors. The approach is suited to many biological studies where the response is multivariate and each predictor is embedded in some biological grouping structure such as gene pathways. Our Bayesian models are connected with penalized regression, and we prove both oracle and asymptotic distribution properties under an orthogonal design. We derive efficient Gibbs sampling algorithms for our models and provide the implementation in a comprehensive R package called MBSGS available on the Comprehensive R Archive Network (CRAN). The performance of the proposed approaches is compared to state-of-the-art variable selection strategies on simulated data sets. The proposed methodology is illustrated on a genetic dataset in order to identify markers grouping across chromosomes that explain the joint variability of gene expression in multiple tissues.

Published
2017

114. Type-II generalized family-wise error rate formulas with application to sample size determination

Author

Delorme, P., Micheaux, P.L., Liquet, Benoit, Riou, J., Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-PR]Mathematics [math]/Probability [math.PR], surgical procedures, operative, TheoryofComputation_LOGICSANDMEANINGSOFPROGRAMS, [MATH.MATH-GT]Mathematics [math]/Geometric Topology [math.GT], education, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], [MATH]Mathematics [math], musculoskeletal system, ComputingMethodologies_ARTIFICIALINTELLIGENCE, human activities, ComputingMilieux_MISCELLANEOUS, health care economics and organizations, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

ACL; International audience; no abstract

Published
2016

115. Variable selection regression of multivariate responses for group data

Author

Liquet, Benoit, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH]Mathematics [math]
Abstract

ACT; International audience

Published
2016

117. Sparse Group Subgroup PLS for Genomics

Author

Liquet, Benoit, R, Thiêbaut, Sutton, M., Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], [MATH.MATH-CA]Mathematics [math]/Classical Analysis and ODEs [math.CA], [MATH]Mathematics [math], [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

INV; International audience

Published
2016

118. A Unified Regularized Group PLS Algorithm Scalable to Big Data

Author

Lafaye de Micheaux, P., M., Sutton, Liquet, Benoit, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH]Mathematics [math]
Abstract

INV; International audience

Published
2016

119. Statistical Methods For Analysing High-Dimensional Data and Massive Data

Author

Liquet, Benoit, Micheaux, P., Delafaye, g, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], [MATH.MATH-CA]Mathematics [math]/Classical Analysis and ODEs [math.CA], [MATH]Mathematics [math], [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

INV; International audience

Published
2016

121. A Unified Regularized Group PLS Algorithm Scalable to Big Data

Author

Liquet, Benoit, M., Sutton, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), and Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], [MATH.MATH-CA]Mathematics [math]/Classical Analysis and ODEs [math.CA], [MATH]Mathematics [math], [INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM], [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

INV; International audience

Published
2016

123. Le logiciel R (2° Éd.) (collection Statistique et probabilités appliquées)

Author

LIQUET Benoît, DROUILHET Rémy, LAFAYE DE MICHEAUX Pierre, LIQUET Benoît, DROUILHET Rémy, and LAFAYE DE MICHEAUX Pierre

Subjects
Statistics--Computer programs--Handbooks, manuals, etc, R (Computer program language)--Handbooks, manuals, etc
Abstract

Ce livre est consacré à un outil désormais incontournable pour l'analyse de données, l'élaboration de graphiques et le calcul statistique : le logiciel R. Après avoir introduit les principaux concepts permettant une utilisation sereine de cet environnement informatique (organisation des données, importation et exportation, accès à la documentation, représentations graphiques, programmation, maintenance, etc.), les auteurs de cet ouvrage détaillent l'ensemble des manipulations permettant la manipulation avec R d'un très grand nombre de méthodes et de notions statistiques : simulation de variables aléatoires, intervalles de confiance, tests d'hypothèses, valeur-p, bootstrap, régression linéaire, ANOVA (y compris répétées), et d'autres encore. Écrit avec un grand souci de pédagogie et clarté, agrémenté de nombreux exercices et travaux pratiques, ce livre accompagnera idéalement tous les utilisateurs de R- et ceci sur les environnements Windows, Macintosh ou Linux - qu'ils soient débutants ou d'un niveau avancé.

Published
2014

126. [HDDA] sparse subspace constrained partial least squares.

Author

Sutton, Matthew, Mengersen, Kerrie, and Liquet, Benoit

Subjects
LEAST squares, PARTIAL least squares regression
Abstract

In this paper, we investigate the objective function and deflation process for sparse Partial Least Squares (PLS) regression with multiple components. While many have considered variations on the objective for sparse PLS, the deflation process for sparse PLS has not received as much attention. Our work highlights a flaw in the Statistically Inspired Modification of Partial Least Squares (SIMPLS) deflation method when applied in sparse PLS regression. We also consider the Nonlinear Iterative Partial Least Squares (NIPALS) deflation in sparse PLS regression. To remedy the flaw in the SIMPLS method, we propose a new sparse PLS method wherein the direction vectors are constrained to be sparse and lie in a chosen subspace. We give insight into this new PLS procedure and show through examples and simulation studies that the proposed technique can outperform alternative sparse PLS techniques in coefficient estimation. Moreover, our analysis reveals a simple renormalization step that can be used to improve the estimation of sparse PLS direction vectors generated using any convex relaxation method. [ABSTRACT FROM AUTHOR]

Published
2019
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129. R2GUESS: A graphics processing unit-based R package for bayesian variable selection regression of multivariate responses

Author

Liquet, Benoit, Bottolo, Leonardo, Campanella, Gianluca, Richardson, Sylvia, Chadeau-Hyam, Marc, Liquet, Benoit, Bottolo, Leonardo, Campanella, Gianluca, Richardson, Sylvia, and Chadeau-Hyam, Marc

Abstract

Technological advances in molecular biology over the past decade have given rise to high dimensional and complex datasets offering the possibility to investigate biological associations between a range of genomic features and complex phenotypes. The analysis of this novel type of data generated unprecedented computational challenges which ultimately led to the definition and implementation of computationally efficient statistical models that were able to scale to genome-wide data, including Bayesian variable selection approaches. While extensive methodological work has been carried out in this area, only few methods capable of handling hundreds of thousands of predictors were implemented and distributed. Among these we recently proposed GUESS, a computationally optimised algorithm making use of graphics processing unit capabilities, which can accommodate multiple outcomes. In this paper we propose R2GUESS, an R package wrapping the original C++ source code. In addition to providing a user-friendly interface of the original code automating its parametrisation, and data handling, R2GUESS also incorporates many features to explore the data, to extend statistical inferences from the native algorithm (e.g., effect size estimation, significance assessment), and to visualize outputs from the algorithm. We first detail the model and its parametrisation, and describe in details its optimised implementation. Based on two examples we finally illustrate its statistical performances and flexibility.

Published
2016

130. Group and sparse group partial least square approaches applied in genomics context

Author

Liquet, Benoit, de Micheaux, Pierre, Hejblum, Borris, Thiebaut, Rodolphe, Liquet, Benoit, de Micheaux, Pierre, Hejblum, Borris, and Thiebaut, Rodolphe

Abstract

Free to read on publisher website Motivation: The association between two blocks of ‘omics’ data brings challenging issues in computational biology due to their size and complexity. Here, we focus on a class of multivariate statistical methods called partial least square (PLS). Sparse version of PLS (sPLS) operates integration of two datasets while simultaneously selecting the contributing variables. However, these methods do not take into account the important structural or group effects due to the relationship between markers among biological pathways. Hence, considering the predefined groups of markers (e.g. genesets), this could improve the relevance and the efficacy of the PLS approach. Results: We propose two PLS extensions called group PLS (gPLS) and sparse gPLS (sgPLS). Our algorithm enables to study the relationship between two different types of omics data (e.g. SNP and gene expression) or between an omics dataset and multivariate phenotypes (e.g. cytokine secretion). We demonstrate the good performance of gPLS and sgPLS compared with the sPLS in the context of grouped data. Then, these methods are compared through an HIV therapeutic vaccine trial. Our approaches provide parsimonious models to reveal the relationship between gene abundance and the immunological response to the vaccine. Availability and implementation: The approach is implemented in a comprehensive R package called sgPLS available on the CRAN.

Published
2016

131. Age-related changes in murine myometrial transcript profile are mediated by exposure to the female sex hormones

Author

Chong, Hsu Phern, Cordeaux, Yolande, Ranjan, Yorain, Richardson, Sylvia, Liquet, Benoit, Smith, Gordon, Charnock-Jones, D. Stephen, Chong, Hsu Phern, Cordeaux, Yolande, Ranjan, Yorain, Richardson, Sylvia, Liquet, Benoit, Smith, Gordon, and Charnock-Jones, D. Stephen

Abstract

In humans, the risk of operative first delivery increases linearly with maternal age. We previously hypothesized that prolonged, cyclical, prepregnancy exposure to estrogen and progesterone contributes to uterine aging. Here, we test this hypothesis. Myometrium was obtained from four groups of virgin mice: (i) 10‐ to 12‐week‐ and 28‐ to 30‐week‐old mice; (ii) 10‐ to 12‐week‐ and 38‐ to 40‐week‐old mice; (iii) 38‐week‐old mice that had an ovariectomy or sham operation early in life; (iv) 38‐week‐old mice that had been treated with progesterone or vehicle containing implants from 8 to 36 weeks. Transcript profiling was carried out using Affymetrix Gene ST 1.1 arrays, and data were normalized. We identified 60 differentially regulated transcripts associated with advancing age (group 1). We validated these changes in group 2 (P for overlap=5.8×10−46). Early ovariectomy prevented the age‐related changes in myometrial transcript profile. Similarly, progesterone‐mediated long‐term ovarian suppression prevented the age‐related changes in myometrial transcript profile. Interferon regulatory factor 7 (Irf7) mRNA was regulated by age and hormonal exposure, and was identified as a predicted regulator of the other differentially expressed transcripts by both promoter sequence and canonical pathway activation analysis (P=8.47×10−5 and P10−10, respectively). Immunohistochemistry demonstrated IRF7 in both mouse and human myometrium. We conclude the following: (i) Myometrial aging in mice is associated with reproducible changes in transcript profile; (ii) these changes can be prevented by interventions which inhibit cyclical changes in the female sex hormones; and (iii) IRF7 may be an important regulator of myometrial function and aging.

Published
2016

132. Dynamical biostatistical models (Chapman and Hall/CRC Biostatistics Series)

Author

Commenges, Daniel, Jacqmin-Gadda, Helene, Ahmadou, Alioum, Proust-Lima, Cecile, Joly, Pierre, Rondeau, Virginie, Liquet, Benoit, Thiebaut, Rodolphe, Commenges, Daniel, Jacqmin-Gadda, Helene, Ahmadou, Alioum, Proust-Lima, Cecile, Joly, Pierre, Rondeau, Virginie, Liquet, Benoit, and Thiebaut, Rodolphe

Abstract

"Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software. The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference. Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results."--Publisher website

Published
2016

134. Comparison of sliced inverse regression approaches for underdetermined cases

Author

COUDRET, Raphaël, LIQUET, Benoit, SARACCO, Jerôme, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Ecole Nationale Supérieure de Cognitique (ENSC), Institut Polytechnique de Bordeaux, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)

Subjects
[STAT.ME]Statistics [stat]/Methodology [stat.ME]
Abstract

International audience; Among methods to analyze high-dimensional data, the sliced inverse regression (SIR) is of particular interest for non-linear relations between the dependent variable and some indices of the covariate. When the dimension of the covariate is greater than the number of observations, classical versions of SIR cannot be applied. Various upgrades were then proposed to tackle this issue such as RSIR and SR-SIR, to estimate the parameters of the underlying model and to select variables of interest. In this paper, we introduce two new estimation methods respectively based on the QZ algorithm and on the Moore-Penrose pseudo-inverse. We also describe a new selection procedure of the most relevant components of the covariate that relies on a proximity criterion between submodels and the initial one. These approaches are compared with RSIR and SR-SIR in a simulation study. Finally we applied SIR-QZ and the associated selection procedure to a genetic dataset in order to find eQTL.

Published
2014

136. Modeles biostatistiques pour l'epidemiologie

Author

Commenges, Daniel, Jacqmin-Gadda, Helene, Ahmadou, Alioum, Joly, Pierre, Liquet, Benoit, Proust-Lima, Cecile, Rondeau, Virginie, Thiebaut, Rodolphe, Commenges, Daniel, Jacqmin-Gadda, Helene, Ahmadou, Alioum, Joly, Pierre, Liquet, Benoit, Proust-Lima, Cecile, Rondeau, Virginie, and Thiebaut, Rodolphe

Abstract

"Cet ouvrage fait le point sur les méthodes disponibles à ce jour pour analyser les données longitudinales. Les principaux résultats de l'inférence statistique et les principaux modèles de régression sont rappelés. Le reste du livre se concentre sur les modèles pour données longitudinales au sens large, c'est-à-dire mesures répétées de variables quantitatives ou qualitatives et histoire d'événements. L'ouvrage est accompagné par le développement d'un ensemble de logiciels R. Il s'agit d'un livre collectif, fruit de l'expérience de recherche et d'enseignement d'une équipe dont les membres ont travaillé ensemble depuis des années. Grâce à cette expérience commune des neuf auteurs, le livre est homogène à la fois dans la vision générale et dans les notations."--Publisher website

Published
2015

137. R: The textbook - Master the language and perform statistical analyses

Author

de Micheaux, Pierre, Drouilhet, Remy, Liquet, Benoit, Pan, Dondong, Tang, Niansheng, Li, Qizha, de Micheaux, Pierre, Drouilhet, Remy, Liquet, Benoit, Pan, Dondong, Tang, Niansheng, and Li, Qizha

Abstract

本书是作者根据近年来在法国格勒诺布尔第二大学统计与商业智能系开设的一系列R课程的讲义提炼和精选而成，在Springer出版了法语版和英文版。 本书内容丰富详实，不仅涵盖了R语言绝大部分的基本操作命令，还由浅入深地介绍了数学和统计领域相当多的教学内容和方法在R中的编程实现，并独具匠心以一种易被读者接受和消化的方式来编排和组织。本书给出了几个实际数据集供读者自由下载，利用这些数据集在R软件中进行各种统计方法的演示。另外，本书还穿插介绍了作者多年在R语言编程方面的一些心得和技巧。 本书的大部分内容适用于各种操作系统（如主流的Windows、Linux、Mac）的用户，既可用作R初学者的教材或自学材料，对高级学者和研究人员也有参考价值。本书的全部习题和实践操作的答案在该书的网站上提供下载服务。 Translation: This book is based on the refinement and selection of a series of R courses offered by the Department of Statistics and Business Intelligence at the University of Grenoble in France in recent years. It is published in French and English in Springer. The content of this book is rich and detailed, which not only covers most of the basic operation commands of R language, but also introduces the programming content and method of quite a lot of teaching content and methods in mathematics and statistics field in R, and it is unique and easy to use. Organized and organized by readers to accept and digest. This book gives several practical data sets for readers to download freely, using these data sets to demonstrate various statistical methods in R software. In addition, the book also interspersed with the author's many years of experience and skills in R language programming. Most of the contents of this book are applicable to users of various operating systems (such as mainstream Windows, Linux, Mac). They can be used as textbooks or self-learning materials for R beginners, and also have reference value for advanced scholars and researchers. The answers to all the exercises and practical exercises in this book are available on the book's website.

Published
2015

138. ClustOfVar : an R package for dimension reduction via clustering of variables. Application in supervised classification and variable selection in gene expressions data

Author

Chavent, Marie, Genuer, Robin, Kuentz-Simonet, Vanessa, Liquet, Benoit, Saracco, Jerôme, Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Equipe de Biostatistique, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR99-ISPED, Aménités et dynamiques des espaces ruraux (UR ADBX), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Chavent, Marie, and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.AP] Statistics [stat]/Applications [stat.AP]
Abstract

International audience; The main goal of this work is to tackle the problem of dimension reduction for high-dimensional supervised classication. The motivation is to handle gene expression data. The proposed method works in 2 steps. First, one eliminates redundancy using clustering of variables, based on the R-package ClustOfVar. This first step is only based on the exploratory variables (genes). Second, the synthetic variables (summarizing the clusters obtained at the first step) are used to construct a classifier (e.g. logistic regression, LDA, random forests). We stress that the first step reduces the dimension and gives linear combinations of original variables (synthetic variables). This step can be considered as an alternative to PCA. A selection of predictors (synthetic variables) in the second step gives a set of relevant original variables (genes). Numerical performances of the proposed procedure are evaluated on gene expression datasets. We compare our methodology with LASSO and sparse PLS discriminant analysis on these datasets.

Published
2013

139. The R Software

Author

Lafaye De Micheaux, Pierre, Drouilhet, Remy, Liquet, Benoit, Drouilhet, Remy, Département de Mathématiques et de statistique [UdeM- Montréal] (DMS), Université du Québec à Montréal = University of Québec in Montréal (UQAM), Fiabilité et Géométrie Aléatoire (FIGAL), Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Epidémiologie et Biostatistique [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2013

140. Comparaison de méthodes basées sur SIR pour des cas sous-déterminés (n < p)

Author

Saracco, Jerome, Coudret, Raphaël, Liquet, Benoit, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Equipe de Biostatistique, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM)-IFR99-ISPED, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)

Subjects
[STAT.ME]Statistics [stat]/Methodology [stat.ME], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2013

141. Sélection de modèles semi-paramétriques

Author

Liquet, Benoit, Liquet, Benoit, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Université Victor Segalen - Bordeaux II, and Commenges daniel

Subjects
sélection de modèles, semi-paramétrique, multiplicité, p-value, [MATH] Mathematics [math], régression logistique, lissage, information de Kullback-Leibler, modèles de survie, épidémiologie, test du score, [MATH]Mathematics [math], bootstrap, validation croisée
Abstract

This thesis develops model selection method for Biostatistical applications, essentially in medicine. The study is structured into three parts. In the first part, we propose a method and a program to determine a significance level for a series of codings of an explanatory variable in logistic regression. This method is illustrated using the data of a study of the relation between cholesterol and dementia. The second part of the thesis presents a general criterion for choosing an estimator in a family of semi-parametric estimators. It is based on a bootstrap estimator of the Kullback-Leibler information. This criterion is applied for modeling the effect of the asbestos on the risk of mesotheliome and we compare it to Birge-Massart selection method. The last part provides a criterion for choosing an estimator in a family of semi-parametric estimators from incomplete data. It is an extension of the criterion presented in the second part. This criterion is based on the expected observed log-likelihood. It is used in particular to select the smoothing parameter for the hazard function and to choose between stratified and unstratified proportional hazards models. An example is given for modeling the effect of sex and educational level on the risk of developing dementia., Cette thèse développe des méthodes de sélection de modèles pour des applications en Biostatistique et plus particulièrement dans le domaine médical. Dans la première partie, nous proposons une méthode et un programme de correction du niveau de signification d'un test lorsque plusieurs codages d'une variable explicative sont essayés. Ce travail est réalisé dans le cadre d'une régression logistique et appliqué à des données sur la relation entre cholestérol et démence. La deuxième partie de la thèse est consacrée au développement d'un critère d'information général permettant de sélectionner un estimateur parmi une famille d'estimateurs semi-paramétriques. Le critère que nous proposons est basé sur l'estimation par bootstrap de l'information de Kullback-Leibler. Nous appliquons ensuite ce critère à la modélisation de l'effet de l'amiante sur le risque de mésothéliome et nous comparons cette approche à la méthode de sélection de Birgé-Massart. Enfin, la troisième partie présente un critère de sélection en présence des données incomplètes. Le critère proposé est une extension du critère developpé dans la deuxième partie. Ce critère, construit sur l'espérance de la log-vraisemblance observée, permet en particulier de sélectionner le paramètre de lissage dans l'estimation lisse de la fonction de risque et de choisir entre des modèles stratifiés et des modèles à risques proportionnels. Nous avons notamment appliqué cette méthode à la modélisation de l'effet du sexe et du niveau d'éducation sur le risque de démence.

Published
2002

143. Attributable risk estimation for adjusted disability multi-state models: application to nosocomial infections

Author

Coeurjolly, Jean-François, Nguile Makao, Molière, Timsit, Jean-François, Liquet, Benoit, Fiabilité et Géométrie Aléatoire (FIGAL), Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Epidémiologie pronostique des cancers et affections graves, Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut d'oncologie/développement Albert Bonniot de Grenoble (INSERM U823), Université Joseph Fourier - Grenoble 1 (UJF)-CHU Grenoble-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), and Université Bordeaux Segalen - Bordeaux 2

Subjects
Multistate models, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Proportional hazard model, Ventilator-associated pneumonia, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Attributable risk/mortality
Abstract

International audience; Attributable risk has become an important concept in clinical epidemiology. In this paper, we suggest to estimate the attributable risk of nosocomial infections using a multistate approach. Recently, a multistate model (called progressive disability model in the literature) has been developed in order to take into consideration both the time-dependency of the risk factor (e.g., nosocomial infections) and the presence of competing risks (e.g., death and discharge) at each time point. However, this approach does not take into account the possible heterogeneity of the study population. In this paper, we investigate an extension of this model and suggest an adjusted disability multistate model including covariates in each transition. This new multistate model has led us to define the concepts of overall and profiled attributable risk. We use a classical semiparametric approach to estimate the model and the new attributable risk. A simulation study is investigated and we show, in particular, that neglecting the presence of covariates when estimating the model can lead to an important bias. The methodology developed in this paper is applied to data on ventilator-associated pneumonia in 12 French intensive care units.

Published
2012

144. Package CPMCGLM : Correction de la pvaleur engendré par la recherche d'un codage d'une variable explicative dans un modèle linéaire généralisé

Author

Riou, Jeremie, Liquet, Benoit, Sciencesconf.org, CCSD, Danone Research, Groupe DANONE, and Université Bordeaux Segalen - Bordeaux 2

Subjects
Méthodes de rééchantillonage, [STAT.AP]Statistics [stat]/Applications [stat.AP], [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Procédure de Bonferroni, [STAT.AP] Statistics [stat]/Applications [stat.AP], Multiplicité des tests, Modèles Linéaires Généralisés, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [STAT.CO]Statistics [stat]/Computation [stat.CO], [STAT.CO] Statistics [stat]/Computation [stat.CO], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Codages Multiples, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

Package CPMCGLM : Correction de la pvaleur engendré par la recherche d'un codage d'une variable explicative dans un modèle linéaire généralisé

Published
2012

145. lcmm : un package R pour l'estimation des modèles mixtes à classes latentes et des modèles conjoints à classes latentes pour données répétées Gaussiennes, ordinales ou curvilinéaires et données de survie

Author

Proust-Lima, Cécile, Diakité, Amadou, Liquet, Benoit, Sciencesconf.org, CCSD, Epidémiologie et Biostatistique [Bordeaux], Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Université Bordeaux Segalen - Bordeaux 2, Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), and Université Bordeaux Segalen - Bordeaux 2

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], modèles conjoints, données censurées, modèles à effets aléatoires, [STAT.CO] Statistics [stat]/Computation [stat.CO], [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], données longitudinales, données hétérogènes, classification, [STAT.AP] Statistics [stat]/Applications [stat.AP], processus latent, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [STAT.CO]Statistics [stat]/Computation [stat.CO], modèles de mélange, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

lcmm : un package R pour l'estimation des modèles mixtes à classes latentes et des modèles conjoints à classes latentes pour données répétées Gaussiennes, ordinales ou curvilinéaires et données de survie

Published
2012

146. Sliced Inverse Regression for stratified population

Author

Chavent, Marie, Kuentz, Vanessa, Liquet, Benoit, Saracco, Jérôme, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Quality control and dynamic reliability (CQFD), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Aménités et dynamiques des espaces ruraux (UR ADBX), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
Abstract

[Departement_IRSTEA]Territoires [TR1_IRSTEA]DTAM; International audience; In this article, we consider a semiparametric single index regression model involving a real dependent variable Y, a p-dimensional quantitative covariable X, and a categorical predictor Z which defines a stratification of the population. This model includes a dimension reduction of X via an index X'b. We propose an approach based on sliced inverse regression in order to estimate the space spanned by the common dimension reduction direction b. We establish root square n-consistency of the proposed estimator and its asymptotic normality. Simulation study shows good numerical performance of the proposed estimator in homoscedastic and heteroscedastic cases. Extensions to multiple indices models, q-dimensional response variable, and/or SIR-alpha based methods are also discussed. The case of unbalanced subpopulations is treated. Finally, a practical method to investigate if there is or not a common direction b is proposed.

Published
2011

147. Classification de variables : le package ClustOfVar

Author

CHAVENT, Marie, KUENTZ, Vanessa, LIQUET, Benoit, SARACCO, Jérôme, Quality control and dynamic reliability (CQFD), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Aménités et dynamiques des espaces ruraux (UR ADBX), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
ALGORITHME, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], CORRELATION, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], METHODOLOGIE, CLASSIFICATION
Abstract

International audience; Le package R ClustOfVar a été développé spécifiquement pour répondre au problème de la classification de variables. Les variables considérées peuvent être toutes quantitatives, toutes qualitatives ou un mélange des deux. Le critère d'homogénéité d'une classe est la somme des carrés des corrélations (pour les variables quantitatives) et des rapports de corrélations (pour les variables qualitatives) à une variable synthétique (quantitative) résumant au mieux les variables de la classe. La variable synthétique qui maximise ce critère est la première composante principale calculée par la méthode PCAMIX. Deux algorithmes sont proposés : un algorithme de classification ascendante hiérarchique et un algorithme de partitionnement de type k-means. Une approche de type bootstrap est proposée pour l'aide au choix du nombre de classes. Les différentes méthodologies sont illustrées sur des données réelles.

Published
2011

148. Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis. Correspondence Analysis and Related Methods

Author

Josse, Julie, Husson, François, Chavent, Marie, Liquet, Benoit, Institut de Recherche Mathématique de Rennes ( IRMAR ), Université de Rennes 1 ( UR1 ), Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -AGROCAMPUS OUEST-École normale supérieure - Rennes ( ENS Rennes ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National des Sciences Appliquées ( INSA ) -Université de Rennes 2 ( UR2 ), Université de Rennes ( UNIV-RENNES ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Recherche Mathématique de Rennes (IRMAR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)

Subjects
Hardware_MEMORYSTRUCTURES, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST], [ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]
Abstract

Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis

Published
2011

150. Le Package R ConvergenceConcepts: Un nouvel outil graphique pour l'étude de quelques modes de convergence de variables aléatoires

Author

Lafaye De Micheaux, Pierre, Liquet, Benoit, Statistique Appliquée et de Géométrie Aléatoire de Grenoble (SAGAG), Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Département de Mathématiques et de Statistiques [UdeM- Montréal], Université de Montréal (UdeM), Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Société Française de Statistique, and Sfds-Hal, Conférence

Subjects
[STAT.TH] Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]
Abstract

International audience; Ce travail tente d'éclairer l'intuition sous-jacente à la convergence de suites de variables aléatoires en utilisant un package R appelé ConvergenceConcepts. Ce package permet de jouer avec de telles suites. Il est ainsi possible de visualiser des trajectoires d'une suite donnée de variables aléatoires et d'étudier, au moyen de représentations dynamiques adaptées, les quatre modes les plus classiques de convergence, à savoir: la convergence presque sûre, la convergence en probabilité, la convergence en moyenne r et la convergence en loi.

Published
2010

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