index.json

[{"authors":["admin"],"categories":null,"content":" Juste GOUNGOUNGA is an Associate Professor of Biostatistics and Health Data Science at the Ecole des Hautes Etudes en Santé Publique ( EHESP) and a researcher at the ARENES Laboratory (UMR CNRS 6051), affiliated with the INSERM U1309 team on \u0026ldquo;Research on Health Services and Management in Health\u0026rdquo; (RSMS). He has held this position since October 2022.\nAt EHESP, Dr. GOUNGOUNGA teaches courses on various topics, including French hospital discharge databases ( PMSI), and supervises students. He is also a member of the CENSUR working survival group. His research specializes in statistical methods for epidemiology, particularly non-communicable diseases like cancer, with a focus on identifying and quantifying health inequalities.\nHe received his medical degree from the University of Ouagadougou in 2012 and transitioned from clinical practice to biostatistics. He completed a Master’s in Public Health, specializing in quantitative and econometric methods for health research, and a Ph.D. in Clinical Research and Public Health with a focus on biostatistics from the University of Aix-Marseille in 2018. His Ph.D. thesis contributed to the extension of relative survival methods in the field of clinical research. While with the Joint Research Unit 1252 SESSTIM (Inserm / IRD / Aix Marseille Université), Dr. GOUNGOUNGA developed the xhaz R package for excess hazard modeling with inappropriate mortality rates. In January 2020, he joined the Burgundy Digestive Cancer Registry/University of Burgundy ( EPICAD team - UMR 1231) as a postdoctoral researcher, supported by the ARC Foundation for Cancer Research. His postdoctoral research focused on estimating time-to-cure for cancer patients, considering disparities in credit and insurance access.\nHis current research explores inequalities in cancer risk and mortality according to smoking status in patients with chronic kidney disease. Here is a link to my updated list of publications: Updated Publications LIST.\n","date":1557964800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1557964800,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://jgoungounga.github.io/author/juste-goungounga/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/juste-goungounga/","section":"authors","summary":"Juste GOUNGOUNGA is an Associate Professor of Biostatistics and Health Data Science at the Ecole des Hautes Etudes en Santé Publique ( EHESP) and a researcher at the ARENES Laboratory (UMR CNRS 6051), affiliated with the INSERM U1309 team on \u0026ldquo;Research on Health Services and Management in Health\u0026rdquo; (RSMS).","tags":null,"title":"Juste Goungounga","type":"authors"},{"authors":null,"categories":null,"content":" Various training courses for careers in public health and the social sector\nBiostatistics and epidemiology UE STA-UNIV 2016-2023\nStatistical programming using R UE Inf Star 2016-2022\nAdvanced survival analysis UE Sta Surv\nMedical practices evaluation UE EVA-PMQS 2016-2019\n","date":1599609600,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1723593600,"objectID":"ccb3b867ade8915ff982e571bef06da6","permalink":"https://jgoungounga.github.io/teaching/example/","publishdate":"2020-09-09T00:00:00Z","relpermalink":"/teaching/example/","section":"teaching","summary":"I am involved on some modules of the Master of Public Health at Aix Marseille University and various modules at the EHESP/Université de Rennes.","tags":null,"title":"Teaching biostatistics and epidemiology","type":"docs"},{"authors":["Juste Goungounga","Nathalie Grafféo","Hadrien Charvat","Roch Giorgi"],"categories":null,"content":"","date":1680307200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680307200,"objectID":"453dd208022969230d9d4f0c537c9bd9","permalink":"https://jgoungounga.github.io/publication/biometrical2023/","publishdate":"2023-04-01T00:00:00Z","relpermalink":"/publication/biometrical2023/","section":"publication","summary":"In the presence of competing causes of event occurrence (e.g., death), the interest might not only be in the overall survival but also in the so-called net survival, that is, the hypothetical survival that would be observed if the disease under study were the only possible cause of death. Net survival estimation is commonly based on the excess hazard approach in which the hazard rate of individuals is assumed to be the sum of a disease-specific and expected hazard rate, supposed to be correctly approximated by the mortality rates obtained from general population life tables. However, this assumption might not be realistic if the study participants are not comparable with the general population. Also, the hierarchical structure of the data can induce a correlation between the outcomes of individuals coming from the same clusters (e.g., hospital, registry). We proposed an excess hazard model that corrects simultaneously for these two sources of bias, instead of dealing with them independently as before. We assessed the performance of this new model and compared it with three similar models, using an extensive simulation study, as well as an application to breast cancer data from a multicenter clinical trial. The new model performed better than the others in terms of bias, root mean square error, and empirical coverage rate. The proposed approach might be useful to account simultaneously for the hierarchical structure of the data and the non-comparability bias in studies such as long-term multicenter clinical trials, when there is interest in the estimation of net survival.\r\n","tags":["Bias Correction","Multicenter Trials","Survival Analysis","Statistical Models"],"title":"Correcting for heterogeneity and non-comparability bias in multicenter clinical trials with a rescaled random-effect excess hazard model","type":"publication"},{"authors":["Laura Botta","Juste Goungounga","Riccardo Capocaccia","Gaelle Romain","Marc Colonna","Gemma Gatta","Olayidé Boussari","Valérie Jooste"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1679702400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1679702400,"objectID":"ed89ebe9f1f960054eef3b19197a8771","permalink":"https://jgoungounga.github.io/publication/bmc2023/","publishdate":"2023-03-25T00:00:00Z","relpermalink":"/publication/bmc2023/","section":"publication","summary":"BACKGROUND: Non-cancer mortality in cancer patients may be higher than overall mortality in the general population due to a combination of factors, such as long-term adverse effects of treatments, and genetic, environmental or lifestyle-related factors. If so, conventional indicators may underestimate net survival and cure fraction. Our aim was to propose and evaluate a mixture cure survival model that takes into account the increased risk of non-cancer death for cancer patients.\rMETHODS: We assessed the performance of a corrected mixture cure survival model derived from a conventional mixture cure model to estimate the cure fraction, the survival of uncured patients, and the increased risk of non-cancer death in two settings of net survival estimation, grouped life-table data and individual patients' data. We measured the model's performance in terms of bias, standard deviation of the estimates and coverage rate, using an extensive simulation study. This study included reliability assessments through violation of some of the model's assumptions. We also applied the models to colon cancer data from the FRANCIM network.\rRESULTS: When the assumptions were satisfied, the corrected cure model provided unbiased estimates of parameters expressing the increased risk of non-cancer death, the cure fraction, and net survival in uncured patients. No major difference was found when the model was applied to individual or grouped data. The absolute bias was \u003c 1% for all parameters, while coverage ranged from 89 to 97%. When some of the assumptions were violated, parameter estimates appeared more robust when obtained from grouped than from individual data. As expected, the uncorrected cure model performed poorly and underestimated net survival and cure fractions in the simulation study. When applied to colon cancer real-life data, cure fractions estimated using the proposed model were higher than those in the conventional model, e.g. 5% higher in males at age 60 (57% vs. 52%).\rCONCLUSIONS: The present analysis supports the use of the corrected mixture cure model, with the inclusion of increased risk of non-cancer death for cancer patients to provide better estimates of indicators based on cancer survival. These are important to public health decision-making; they improve patients' awareness and facilitate their return to normal life.\r","tags":["Cancer","Cure Model","Non-Cancer Mortality","Survival Analysis","Statistical Models"],"title":"A new cure model that corrects for increased risk of non-cancer death: analysis of reliability and robustness, and application to real-life data","type":"publication"},{"authors":["Noémi Reboux","Valérie Jooste","Juste Goungounga","Michel Robaszkiewicz","Jean-Baptiste Nousbaum","Anne-Marie Bouvier"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1664755200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664755200,"objectID":"c15430467f9adf09d750889af3000bf7","permalink":"https://jgoungounga.github.io/publication/jama2022/","publishdate":"2022-10-03T00:00:00Z","relpermalink":"/publication/jama2022/","section":"publication","summary":"IMPORTANCE: Although treatment and prognosis of synchronous liver metastases from colorectal cancer are relatively well known, a comparative description of the incidence, epidemiological features, and outcomes of synchronous and metachronous liver metastases is lacking. The difference in prognosis between patients with synchronous and metachronous liver metastases is controversial.\r\n\r\nOBJECTIVE: To investigate temporal patterns in the incidence and outcomes of synchronous vs metachronous liver metastases from colorectal cancer.\r\n\r\nDESIGN, SETTING, AND PARTICIPANTS: This population-based cohort study used information from a French regional digestive cancer registry accounting for 1,082,000 inhabitants. A total of 26,813 patients with a diagnosis of incident colorectal adenocarcinoma diagnosed between January 1, 1976, and December 31, 2018, were included. Data were analyzed from February 7 to May 20, 2022.\r\n\r\nMAIN OUTCOMES AND MEASURES: Age-standardized incidence was calculated. Univariate and multivariate net survival analyses were performed.\r\n\r\nRESULTS: Of 26,813 patients with colorectal cancer (15,032 men [56.1%]; median [IQR] age, 73 [64-81] years), 4,546 (17.0%) presented with synchronous liver metastases. The incidence rate of synchronous liver metastases was 6.9 per 100,000 inhabitants in men and 3.4 per 100,000 inhabitants in women, with no significant variation since 2000. The 5-year cumulative incidence of metachronous liver metastases decreased from 18.6% (95% CI, 14.9%-22.2%) during the 1976 to 1980 period to 10.0% (95% CI, 8.8%-11.2%) during the 2006 to 2011 period. Cancer stage at diagnosis was the strongest risk factor for liver metastases; compared with patients diagnosed with stage II cancer, patients with stage III cancer had a 2-fold increase in risk (subdistribution hazard ratio, 2.42; 95% CI, 2.08-2.82) for up to 5 years. Net survival at 1 year was 41.8% for synchronous liver metastases and 49.9% for metachronous metastases, and net survival at 5 years was 6.2% for synchronous liver metastases and 13.2% for metachronous metastases. Between the first (1976-1980) and last (2011-2016) periods, the adjusted ratio of death after synchronous and metachronous metastases was divided by 2.5 for patients with synchronous status and 3.7 for patients with metachronous status.\r\n\r\nCONCLUSIONS AND RELEVANCE: In this study, the incidence of colorectal cancer with synchronous liver metastases changed little over time, whereas there was a 2-fold decrease in the probability of developing metachronous liver metastases. Survival improved substantially for patients with metachronous liver metastases, whereas improvement was more modest for those with synchronous metastases. The differences observed in the epidemiological features of synchronous and metachronous liver metastases from colorectal cancer may be useful for the design of future clinical trials.\r\n","tags":["Colorectal Cancer","Liver Metastases","Survival Analysis","Epidemiology","Oncology"],"title":"Incidence and Survival in Synchronous and Metachronous Liver Metastases From Colorectal Cancer","type":"publication"},{"authors":["Marc Colonna","Pascale Grosclaude","Anne Marie Bouvier","Juste Goungounga","Valérie Jooste"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1664582400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664582400,"objectID":"6e970403ef22f4671110a673b054d9a6","permalink":"https://jgoungounga.github.io/publication/cancer2022/","publishdate":"2022-10-01T00:00:00Z","relpermalink":"/publication/cancer2022/","section":"publication","summary":"BACKGROUND: Cancer prevalence is heterogeneous because it includes individuals who are undergoing initial treatment and those who are in remission, experiencing relapse, or cured. The proposed statistical approach describes the health status of this group by estimating the probabilities of death among prevalent cases. The application concerns colorectal, lung, breast, and prostate cancers and melanoma in France in 2017.\r\n\r\nMETHODS: Excess mortality was used to estimate the probabilities of death from cancer and other causes.\r\n\r\nRESULTS: For the studied cancers, most deaths from cancer occurred during the first 5 years after diagnosis. The probability of death from cancer decreased with increasing time since diagnosis except for breast cancer, for which it remained relatively stable. The time beyond which the probability of death from cancer became lower than that from other causes depended on age and cancer site: for colorectal cancer, it was 6 years after diagnosis for women (7 years for men) aged 75-84 and 20 years for women (18 years for men) aged 45-54 years, whereas cancer was the major cause of death for women younger than 75 years whatever the time since diagnosis for breast and for all patients younger than 75 years for lung cancer. In contrast, deaths from other causes were more frequent in all the patients older than 75 years. Apart from breast cancer in women younger than 55 years and lung cancer in women older than 55 years and men older than 65 years, the probability of death from cancer among prevalent cases fell below 1%, with varying times since diagnosis.\r\n\r\nCONCLUSIONS: The authors' approach can be used to better describe the burden of cancer by estimating outcomes in prevalent cases.\r\n","tags":["Cancer","Mortality Dynamics","Prevalence","Health Status","Statistical Models"],"title":"Health status of prevalent cancer cases as measured by mortality dynamics (cancer vs. noncancer): Application to five major cancer sites","type":"publication"},{"authors":null,"categories":["R"],"content":" Packages utiles library(\u0026quot;parallel\u0026quot;) library(\u0026quot;foreach\u0026quot;) library(\u0026quot;microbenchmark\u0026quot;) library(\u0026quot;tidyverse\u0026quot;) Contexte Supposons que l’on souhaite estimer la qualité de prédiction d’un modèle linéaire, ici un modèle linéaire pour la régression de la largeur d’une pétale sur une longueur sur le jeu de données iris de R. On peut utiliser la technique du leave-one-out qui consiste à estimer l’erreur de généralisation : erreur observée pour des nouveaux individus qui viennent de la même distribution que les individus utilisés pour apprendre le modèle\nPrincipe de la technique du leave-one-out estimation du modèle avec tous les individus sauf un, prédiction pour cet individu Calcul de l’erreur quadratique (Prediction sum of squares statistic) entre la prédiction et la valeur connue répéttition de l’opération pour chacun des individus sommation des erreurs obtenues Implémentation leave_one_out \u0026lt;- function(i) { model \u0026lt;- lm(Petal.Width ~ Petal.Length, data = iris[-i,]) pred.petal.width \u0026lt;- predict(model, data.frame(Petal.Length = iris[i, \u0026quot;Petal.Length\u0026quot;])) return((pred.petal.width - iris[i, \u0026quot;Petal.Width\u0026quot;]) ^ 2) } Appelons la fonction pour 100 individus du jeu de données iris.\nmicrobenchmark::microbenchmark( lapply(1:100, FUN = function(i)leave_one_out(i)), times = 10 ) ## Unit: milliseconds ## expr min lq mean ## lapply(1:100, FUN = function(i) leave_one_out(i)) 185.4462 193.1641 214.1645 ## median uq max neval ## 201.862 231.1189 278.0691 10 Chaque appel de la fonction est indépendant. Proposons donc un test de la fonction par parrallèle.\nleave_one_out2 \u0026lt;- function(){ library(\u0026quot;parallel\u0026quot;) output \u0026lt;- foreach(i = 1:100) %dopar% { model \u0026lt;- lm(Petal.Width ~ Petal.Length, data = iris[-i,]) pred.petal.width \u0026lt;- predict(model, data.frame(Petal.Length = iris[i, \u0026quot;Petal.Length\u0026quot;])) return((pred.petal.width - iris[i, \u0026quot;Petal.Width\u0026quot;]) ^ 2) } return(output) } library(parallel) cl \u0026lt;- detectCores() cl2 \u0026lt;- makeCluster(cl - 1) # a adapter suivant le nombre de coeurs de ta machine microbenchmark(lapply(1:100, FUN = function(i)leave_one_out(i)),leave_one_out2(), times = 10) %\u0026gt;% print(digits = 3) ## Warning: executing %dopar% sequentially: no parallel backend registered ## Unit: milliseconds ## expr min lq mean median uq max ## lapply(1:100, FUN = function(i) leave_one_out(i)) 185 189 231 203 232 376 ## leave_one_out2() 219 237 271 243 252 507 ## neval cld ## 10 a ## 10 a stopCluster(cl2) ","date":1575504000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1575504000,"objectID":"a34aada056b45d92a90ed46ea1a27f95","permalink":"https://jgoungounga.github.io/talks/leave_one_out_test/","publishdate":"2019-12-05T00:00:00Z","relpermalink":"/talks/leave_one_out_test/","section":"talks","summary":"Packages utiles library(\u0026quot;parallel\u0026quot;) library(\u0026quot;foreach\u0026quot;) library(\u0026quot;microbenchmark\u0026quot;) library(\u0026quot;tidyverse\u0026quot;) Contexte Supposons que l’on souhaite estimer la qualité de prédiction d’un modèle linéaire, ici un modèle linéaire pour la régression de la largeur d’une pétale sur une longueur sur le jeu de données iris de R.","tags":["R Markdown","programming","parallel"],"title":"Leave one out","type":"talks"},{"authors":null,"categories":null,"content":"","date":1559606400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1559606400,"objectID":"d1311ddf745551c9e117aa4bb7e28516","permalink":"https://jgoungounga.github.io/project/external-project/","publishdate":"2019-06-04T00:00:00Z","relpermalink":"/project/external-project/","section":"project","summary":"using Rpubs to present differences betwenn chisq.test() and prop.test() R functions via `external_link`.","tags":["Other"],"title":"External Project","type":"project"},{"authors":["Juste Goungounga","Célia Touraine","Nathalie Grafféo","Roch Giorgi","the CENSUR working survival group"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1557964800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1557964800,"objectID":"a456862a2d83d35e408a33d2783b7885","permalink":"https://jgoungounga.github.io/publication/bmc2019/","publishdate":"2019-05-16T00:00:00Z","relpermalink":"/publication/bmc2019/","section":"publication","summary":"In conclusion, the new RBS model allows estimating net survival in clinical trials. It corrects the biases of cause-of-death misclassification and of selection effect on the expected mortality in the general population. This makes it particularly useful in clinical trials with long follow-ups. With the RBS model, the researcher obtains accurate estimates of the excess hazard and, therefore, of net survival; however, he/she should check the strong assumption of homogeneous selection. Finally, the RBS model paves the way for new methodological developments in the field of net survival methods in multicenter clinical trials.","tags":["Source Themes"],"title":"Correcting for misclassification and selection effects in estimating net survival in clinical trials","type":"publication"},{"authors":[],"categories":[],"content":"Create slides in Markdown with Academic Academic | Documentation\nFeatures Efficiently write slides in Markdown 3-in-1: Create, Present, and Publish your slides Supports speaker notes Mobile friendly slides Controls Next: Right Arrow or Space Previous: Left Arrow Start: Home Finish: End Overview: Esc Speaker notes: S Fullscreen: F Zoom: Alt + Click PDF Export: E Code Highlighting Inline code: variable\nCode block:\nporridge = \u0026quot;blueberry\u0026quot; if porridge == \u0026quot;blueberry\u0026quot;: print(\u0026quot;Eating...\u0026quot;) Math In-line math: $x + y = z$\nBlock math:\n$$ f\\left( x \\right) = ;\\frac{{2\\left( {x + 4} \\right)\\left( {x - 4} \\right)}}{{\\left( {x + 4} \\right)\\left( {x + 1} \\right)}} $$\nFragments Make content appear incrementally\n{{% fragment %}} One {{% /fragment %}} {{% fragment %}} **Two** {{% /fragment %}} {{% fragment %}} Three {{% /fragment %}} Press Space to play!\nOne **Two** Three A fragment can accept two optional parameters:\nclass: use a custom style (requires definition in custom CSS) weight: sets the order in which a fragment appears Speaker Notes Add speaker notes to your presentation\n{{% speaker_note %}} - Only the speaker can read these notes - Press `S` key to view {{% /speaker_note %}} Press the S key to view the speaker notes!\nOnly the speaker can read these notes Press S key to view Themes black: Black background, white text, blue links (default) white: White background, black text, blue links league: Gray background, white text, blue links beige: Beige background, dark text, brown links sky: Blue background, thin dark text, blue links night: Black background, thick white text, orange links serif: Cappuccino background, gray text, brown links simple: White background, black text, blue links solarized: Cream-colored background, dark green text, blue links Custom Slide Customize the slide style and background\n{{\u0026lt; slide background-image=\u0026quot;/img/boards.jpg\u0026quot; \u0026gt;}} {{\u0026lt; slide background-color=\u0026quot;#0000FF\u0026quot; \u0026gt;}} {{\u0026lt; slide class=\u0026quot;my-style\u0026quot; \u0026gt;}} Custom CSS Example Let\u0026rsquo;s make headers navy colored.\nCreate assets/css/reveal_custom.css with:\n.reveal section h1, .reveal section h2, .reveal section h3 { color: navy; } Questions? Ask\nDocumentation\n","date":1549324800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1549324800,"objectID":"0e6de1a61aa83269ff13324f3167c1a9","permalink":"https://jgoungounga.github.io/slides/example/","publishdate":"2019-02-05T00:00:00Z","relpermalink":"/slides/example/","section":"slides","summary":"An introduction to using Academic's Slides feature.","tags":[],"title":"Slides","type":"slides"},{"authors":["Juste Goungounga","Roch Giorgi"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1517184000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1517184000,"objectID":"0f45bf041f55a6b825b5ee68a9f5d943","permalink":"https://jgoungounga.github.io/publication/jcrco2018/","publishdate":"2018-01-29T00:00:00Z","relpermalink":"/publication/jcrco2018/","section":"publication","summary":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","tags":["Source Themes"],"title":"Commentary on 'Survival benefit of mantle cell lymphoma patients enrolled in clinical trials; a joint study from the LYSA group and French cancer registries'.","type":"publication"},{"authors":["Juste Goungounga","Jean Gaudard","Marc Colonna","Roch Giorgi"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1476230400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1476230400,"objectID":"ea776cc3f59ca5b334d77fe79a7075b1","permalink":"https://jgoungounga.github.io/publication/bmc2016/","publishdate":"2016-10-12T00:00:00Z","relpermalink":"/publication/bmc2016/","section":"publication","summary":"In spatial analysis of cancer incidence, SpODT and HBSM may be used not only for cluster detection but also for searching for confounding or etiological factors in small areas. Moreover, the multivariate HBSM offers a flexible and meaningful modeling of spatial variations; it shows plausible previously unknown associations between various cancers.","tags":["Source Themes"],"title":"Impact of socioeconomic inequalities on geographic disparities in cancer incidence - comparison of methods for spatial disease mapping","type":"publication"},{"authors":["Juste Goungounga"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1466161200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1466161200,"objectID":"945ed291e4aefa914a848db069567644","permalink":"https://jgoungounga.github.io/talks/writing-technical-content/","publishdate":"2016-06-17T12:00:00Z","relpermalink":"/talks/writing-technical-content/","section":"talks","summary":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","tags":["R Markdown","Survival analysis","Clinical trials","Net Survival"],"title":"Webinar on 'Estimation de la survie nette dans les essais cliniques - Intérêts des méthodes utilisées dans les études populationnelles'.","type":"talks"},{"authors":null,"categories":null,"content":"To be added\n","date":1461715200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1461715200,"objectID":"8f66d660a9a2edc2d08e68cc30f701f7","permalink":"https://jgoungounga.github.io/project/internal-project/","publishdate":"2016-04-27T00:00:00Z","relpermalink":"/project/internal-project/","section":"project","summary":"See talks page.","tags":["Statistics"],"title":"Internal Project","type":"project"},{"authors":["Juste Goungounga","Jean Gaudart","Marc Colonna","Roch Giorgi"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1432166400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1432166400,"objectID":"69425fb10d4db090cfbd46854715582c","permalink":"https://jgoungounga.github.io/publication/conference-paper/","publishdate":"2015-05-21T00:00:00Z","relpermalink":"/publication/conference-paper/","section":"publication","summary":"(**Introduction**) Plusieurs méthodes statistiques permettent d’évaluer l’impact d’un facteur sur la répartition spatiale d’une maladie donnée. Cependant, leur fiabilité est souvent remise en question et des variations spatiales réelles peuvent être confondues à celles issues d’un bruit statistique, surtout lorsque les indicateurs sanitaires sont disponibles à une échelle très fine mais très variable comme celle des communes. Lorsque les incidences sont cartographiées, les comparaisons d’incidence entre aires géographiques ne sont valides que lorsque certains tiers facteurs, associés à la survenue de la maladie, ne diffèrent pas significativement entre les unités spatiales. Dans le domaine du cancer, plusieurs études ont démontré qu’il existe un lien entre les variations géographiques de certains cancers et le niveau socioéconomique des individus. L’objectif de notre travail était d’évaluer l’impact de la prise en compte d’un indicateur du niveau socioéconomique dans la détection de clusters spatiaux de cancers, en comparant, de manière empirique, différentes méthodes.","tags":["Source Themes"],"title":"Impact des inégalités socioéconomiques sur les variations spatiales de l’incidence du cancer en Isère - comparaison de méthodes de détections de clusters spatiaux","type":"publication"}]