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6 results on '"Dejardin, Olivier"'

3. Apport des profils de croyances en santé sur le dépistage du cancer colorectal

Author

Vallet, Fanny, Guillaume, Elodie, Dejardin, Olivier, Guittet, Lydia, Berchi, Célia, Launoy, Guy, Christophe, Veronique, Sciences Cognitives et Sciences Affectives (SCALab) - UMR 9193 (SCALab), Université de Lille-Centre National de la Recherche Scientifique (CNRS), and Laboratoire Sciences Cognitives et Sciences Affectives - UMR 9193 (SCALab)

Subjects
[SCCO]Cognitive science
Published
2016

4. Social inequalities in health from observational studies to intervention: can the patient navigator reduce social inequalities in cancer patients?

Author

Dejardin, Olivier, Berchi, Célia, Mignon, Astrid, Pornet, Carole, Guillaume, Elodie, Guittet, Lydia, Bouvier, Véronique, Sailly, M., SALINAS, Agnès, Christophe, Véronique, Launoy, Guy, Cancers et Populations : Facteurs de Risque, Depistage, Pratiques Diagnostiques et Therapeutiques, Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Recherche en Sciences Cognitives et Affectives (URECA), Université de Lille, Sciences Humaines et Sociales-PRES Université Lille Nord de France, Centre d'étude et de recherche sur les risques et les vulnérabilités (CERREV), Normandie Université (NU)-Normandie Université (NU), Maison européenne des sciences de l’homme et de la société - UAR 3185 (MESHS), Université d'Artois (UA)-Université de Picardie Jules Verne (UPJV)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Fédération Universitaire et Polytechnique de Lille-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Cancéropôle nord-ouest, PRES Université Lille Nord de France-Université de Lille, Sciences Humaines et Sociales, and Maison européenne des sciences de l’homme et de la société - USR 3185 (MESHS)

Subjects
Health Services Needs and Demand, Intervention, [SDV.CAN]Life Sciences [q-bio]/Cancer, Colorectal cancer, Cancer colorectal, Patient navigator, Socioeconomic Factors, Dépistage, Neoplasms, Patient-Centered Care, [SCCO.PSYC]Cognitive science/Psychology, Accompagnateur, Humans, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [SHS.LANGUE]Humanities and Social Sciences/Linguistics, Inégalités sociales, Social inequalities, Cancer
Abstract

International audience; The impact of social factors on healthcare inequality is well-recognized in many industrialized countries and involves a wide range of pathological conditions (cardiovascular disease, cancer, etc.). In general, the poorest indicators of health are observed in socially disadvantaged populations. Beyond this observation is the question of actions taken to prevent the formation ofsocial inequality in healthcare. The purpose of this work was to evaluate the potential contribution of an intervention tool called the ‘‘patient navigator’’, used in English-speaking countries and to determine its feasibility in France; Des inégalités sociales de santé ont été rapportées dans l’ensemble des pays industrialisés et pour un très grand nombre de pathologies (maladies cardiovasculaires, cancers, etc...). Les patients issus des milieux les plus défavorisés présentent généralement les plus mauvais indicateurs de santé. Au-delà du constat se pose dès lors la question des moyens d’action permettant de lutter efficacement contre la formation des inégalités sociales de santé. L’objectif de cet article est d’étudier l’intérêt potentiel d’une méthode de lutte contre les inégalités sociales en cancérologie, couramment dénommée « patient navigator » dans les pays anglosaxons (accompagnateur d’un patient en français) et d’évaluer la faisabilité de sa mise en place en France.

Published
2011
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6. How to deal with missing data? Multiple imputation by chained equations: recommendations and explanations for clinical practice

Author

Legendre B, Cerasuolo D, Dejardin O, and Boyer A

Subjects
Animals, Mice, Computer Simulation, Biomedical Research
Abstract

The presence of missing data, a constant problem in medical research, has several consequences: systematic loss of power, associated or not with a reduction in the representativeness of the sample analyzed. There are three types of missing data: 1) missing completely at random (MCAR); 2) missing at random (MAR); 3) missing not at random (MNAR). Multiple imputation by chained equations allows for the correct handling of missing data under the MCAR and MAR assumptions. It allows to simulate for each missing data j, a number m of simulated values which seem plausible with regard to the other variables. A random effect is included in this simulation to express the uncertainty. Several data sets are thus created and analyzed individually, in an identical way. Then the estimators of each data set are combined to obtain a global estimator. Multiple imputation increases power, corrects for some biases and has the advantage of being applicable to many types of variables. Complete case analysis should no longer be the norm. The objective of this guide is to help the reader in conducting an analysis with multiple imputed data. We cover the following points: the different types of missing data, the different historical approaches to handling them, and then we detail the multiple imputation method using chained equations. We provide a code example for the mice package of R®.

Published
2023
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