Axe SCALE-EPI, CIC 1402 - Poitiers
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.
Topics of interest
The individual randomized clinical trial has 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 of random selection, etc. DHSs therefore represent research infrastructures with considerable potential for evidence-based medicine. In this context, two complementary dynamics can be identified within the SCALE-EPI axis of CIC 1402.
The first is aimed at removing the technological barriers to the creation of high-quality EDS for research purposes. It is being driven in particular by the Poitou-Charentes general cancer registry, 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 help build the complementarity 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.
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CIC-EC 1433 - Inserm
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