SERVIER CAMPUS

Internship in Real World Evidence: large scale analytics through the OMOP/OHDSI framework (M/F)


Published on
14 Nov2019
Duration

6 mois

Reference

JNBE_20MVD_KM_RWE

Remuneration

By profile

Location

Suresnes

Start date

January 2020


Job description

Within the Pharmacoepidemiology and Real World Evidence (RWE) Department (13 people, including epidemiologists, data analysts and biostatisticians), our goal is to design and execute RWE studies to support our portfolio of drugs through their lifecycle from early clinical development (natural history of disease, treatment pathways, inform inclusion/exclusion criteria for clinical trials) to post-authorisation phase (safety signal and risk assessment, comparative effectiveness).

Real World Data (RWD) coming from Electronic Health Record (EHR) systems and other types of health databases is used largely to generate RWE and bring invaluable insights into patient health care.

Through the OMOP/OHDSI framework, open source tools are available to develop interoperability across diverse data sources by standardization of data in a common data model and allow large scale analytics, as well as transparency, reproducibility and reliability of RWE studies.

In relation to the EHDEN collaborative international project based on OMOP and OHDSI tools, we wish to evaluate the analytical functionalities offered by these tools to conduct RWE studies based on secondary use of EHR.

Missions

  • Develop knowledge of the OHDSI’s analytics tools to analyse standardized, patient-level, observational data in the common data format OMOP
  • Use these tools to design and execute pilot RWE studies to answer different types of use cases: characterization, population-level estimation, and patient-level estimation, using available data (synthetic and/or real world data)
  • Investigate the pros and cons of different approaches to implement the RWE studies: 1) mainly using the interactive analysis platform ATLAS; 2) developing the analysis in R by making use of available packages; 3) writing custom code when maximal flexibility is needed (optional)

Required profile

Master 2 student in epidemiology, pharmacoepidemiology, biomedical informatics or data science

Good knowledge in R programming. Practical experience in observational health data analysis is a plus

Good written and verbal English to interact with the internal and external RWE community

Apply now

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