CIC 1402 Poitiers - SCALE-EPI axis




Who are we ?


Under the aegis of the NOVA healthcare cooperation group (Nouvelle Aquitaine), Poitiers University Hospital has won the AAP France 2030 award for the structuring of Health Data Warehouses (EDS). The aim is to build a partially shared infrastructure with the CHUs of Bordeaux (lead partner) and Limoges. At the same time, CIC Inserm 1402 (2 rue de la Milétrie, 86000 Poitiers), headed by Pr. PJ Saulnier, is proposing a new line of research focusing on methods for constructing and analyzing real-life data. It is called SCALE-EPI (methodS in ClinicAL rEsearch & EPIdemiology). Most of the epidemiological and statistical researchers at the Poitiers university hospital campus have joined this emerging area. It is coordinated by Pr. S Ragot.

Individual randomized clinical trials have often been presented as the only method for finding causality. But this paradigm is waning in proportion to its increasingly accepted limitations: lack of external validity, low power of subgroup analyses, small numbers that do not guarantee comparability, difficulties with random selection, etc. DHSs therefore represent research infrastructures with considerable potential for evidence-based medicine. In this context, two complementary dynamics can be distinguished within the SCALE-EPI axis of CIC 1402.

The first aims to remove the technological barriers to the creation of high-quality DHS for research purposes. The Poitou-Charentes general cancer registry is a prime example, whether for reliable estimates of public health indicators (1) or for comparisons of therapeutic alternatives in high-definition sub-cohorts (2). Bringing together hospital DHSs and registries will also enable us to build on the complementary nature of these information systems.

The second dynamic focuses on new statistical methods needed for causality from these real-life data: machine learning to emulate clinical trials from high-dimensional data (3), time-dependent propensity scores to emulate clinical trials with time-dependent treatments (4), multi-state models to model health trajectories and simulate the impact of intervention (5). Thanks to these methods, a new project aims to use historical data such as EDS to increase the performance of future clinical trials and reduce the number of subjects required.


Main publications



Contact us

CIC Inserm 1402

2 rue de la Milétrie, 86000 Poitiers


Pr Stéphanie Ragot :

For CIC 1402 :

Pr Pierre Jean Saulnier :





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