IMAGeS team: IMages, leArning, Geometry and Statistics

Erik-André Sauleau

From IMAGeS team: IMages, leArning, Geometry and Statistics
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University Professor - Hospital Practitioner

  • Head of MiBISA research group
ICube - MIV
300 Bd Sébastien Brant
BP 10413
67412 Illkirch CEDEX - France

Tel : + 33 (0) 3 68 85 39 51
Fax : + 33 (0)
Bureau : ???

Courriel : ea(dot)sauleau(at)unistra(dot)fr
Page personelle : http://


The Laboratory of Biostatistic and Medical Informatics (LBIM)

The LBM is a ward in the Faculty of Medicine. It provides teaching and research missions in analyses of health data. The lectures are primarily intended for medical students, students of paramedical schools and more generaly students enrolled in courses in which these analyzes are needed (masters in public health for example).

In research, most of LBIM's work is structured around Bayesian inference, methods for clinical research (new study patterns, specific statistical analyzes ...) and spatial analysis. The team was also interested until recently in the theory, software implementation and application of PLS ​​methods in medicine, particularly in the context of allelotyping.

Description of my research activities

My main research interests relate to the Bayesian inference in its epistemological, historical and methodological aspects. Implemented Bayesian models are then applied to different types of health data:

  • Clinical Research. In the Bayesian toolbox for planning and analysing clinical trials, calculation techniques for sample size and predictive probability allow to finely modulate the number of subjects to be recruited into a study.
  • Geographical and spatial models. The spatial location or even spaciotemporal in health data is becoming more and more common. It's the same with questions about possible links between exposure to given risk factors and occurrence of health events. Almost all of the spatial models can be integrated into the general framework of generalized additive models with a spatial component, a particular case of "Structured Additive Regressions" or StAR models.

Some of these activities are structured in a research group on the causality in epidemiology. The overall objective of this group is enhance knowledge on the philosophical and epidemiological/biostatistics foundations of causation and causal modeling, with special anchoring on the history of these approaches and their historiography. We aim to strengthend an international scientific network, eminently multidisciplinary and interdisciplinary: INCEPTION (for Interdisciplinary Network on Causality in EPidemiological investigaTIONs). This network begins to evolve around a geographical axis of three places and teams in Strasbourg, Cambridge (UK) and Cagliari (I).


  • Responsible for the University Diploma (Diplôme d'Université, DU): Biostatistics "From frequentist to Bayesian statistics"
  • Responsible for Informatics Certificate (C2i), Level 1 (Faculty of Medicine)
  • Tutorial classes (travaux dirigés) in statistics in the first common year for health studies (PACES)
  • About 20 hours in introductory lectures in biostatistics and critical reading of scientific papers (paramedics)
  • Lectures in Bayésian inference (Master in public health)