Section: Application Domains

Data analysis and local regression

Our expertise in data analysis and advanced statistics methods has given raise no a wide number of interdisciplinary collaborations. Among those, here are the most challenging at a scientific level:

(i) Health inequalities: We have recently developed a statistical procedure in order to create a neighborhood socioeconomic index and investigate its influence on health inequalities. The study setting is composed with 3 major French metropolitan areas (Lille, Lyon and Marseille), and we collaborate for this project with a medical team at EHESP (Ecole des Hautes Etudes en Santé Publique) lead by D. Zmirou (see [19] for further details).

(ii) Fetal pathology: An ongoing work concerning local regression techniques is related to Fetal Biometry, an investigation line suggested by a collaboration between our team and the Centre de Placentologie et Foetopathologie de la Maternité Régionale de Nancy, under the direction of Professor Bernard Foliguet. The methods involved in Fetal Biometry are usually based on the comparison of some measured values with the predicted values derived from reference charts or equations in a normal population. However, it happens that maternal and pregnancy characteristics have a significant influence on in-utero Fetal Biometry. We will thus produce some models allowing to construct customized fetal biometric size charts. In order to evaluate them, classical and polynomial regression can be used, but they are not the most appropriate to the kind data we have to handle. Hence, we plan to use local regression estimation in order to perform such an evaluation.

(iii) Cohorts analysis: Some medical teams in Nancy are faced with an overwhelming amount of data, for which a serious statistical assessment is needed. Among those let us mention the INSERM team of Pr. Jean-Louis Guéant and the Inria team Orpailleur (particularly with Marie-Dominique Desvignes and Malika Smail). The goal of this collaboration is to extract biological markers for different diseases (cognitive decline; inflammatory intestinal diseases; liver cancer). To this aim, the INSERM team provides us with several data cohorts with a high number of variables and subjects. As in many instances in Biostatistics, one is then faced with a very high dimensional data, from which we hope to extract a reduced number of significant variables allowing to predict the cardiovascular risk accurately. Moreover, these characters should be meaningful to practitioners. The objective for us is thus to design an appropriate variable selection, plus a classification procedure in this demanding context. Let us highlight an original feature of this collaboration: it combines our own data analysis techniques with those developed by the Orpailleur team, based on symbolic tools. We hope that this experience will enrich both points of view and give raise to new methods of data analysis.