Module
Target trials emulation using observational data
Teaching conducted in English
Targeted trial emulation aims to estimate the effects of a treatment by simulating randomized controlled trials using real-world observational data.
In this module, you will learn about the specific characteristics of randomized controlled trials and observational studies, as well as the concept of causal inference. You will then be introduced to the techniques of targeted trials through lectures by specialists in the field and a comprehensive practical session to apply the concepts learned.
General information
About the course
Dates
Prices
Programme
Day 1
– Introduction to target trial emulation
– A principled introduction to causal inference: causal estimands and methods for time-fixed treatment decisions. The focus here will be on the following points: causal assumptions, g-computation, propensity score matching, inverse probability of treatment weighting and double robust estimators.
Day 2
– Comparing initiators and non-initiators
– Practical implementation of causal inference for time fixed treatment decisions (with R)
Day 3
– Pratical cases based on existing studies
– Practical target trial emulation with a grace period (with R)
Speakers
Pr. Raphaël Porcher
Raphaël Porcher is a Professor of Biostatistics with expertise in innovative statistical methods for causal inference and personalized medicine, and emulation of complex target trials with observational data, and a chair in the PR[AI]RIE-PSAI AI cluster. As PI or Co-Investigator of several nationally-funded (e.g., French Agence Nationale de la Recherche) or European Union-funded (e.g., H2020 programs) projects, his research focuses on 1) causal inference methods to estimate the effect of interventions with real-world data, especially in the case of time-varying treatments; 2) statistical methods for personalized medicine, including clinical trials designs and the analysis of observational studies; 3) methodological issues in target trial emulation; and 4) the use of observational data for drug assessment by regulators of heath technology assessment. He also has long-lasting experience in designing and analyzing studies aiming to emulate a target trial, including complex situations of treatment durations or with time-varying treatments. At Université Paris Cité level, Raphaël Porcher is the director of the college of doctoral studies, and he is also the President of the national network of doctoral colleges (Réseau National des Collèges Doctoraux, France PhD).
Pr. Viet-Thi Tran
Viet-Thi Tran is a University Professor/Hospital Practitioner (PUPH) in epidemiology at Université Paris Cité and at Assistance Publique Hôpitaux de Paris. He has two main research themes. The first is the development of “Minimally Disruptive Medicine” for patients suffering from chronic diseases and multimorbidity. He is working to extend this concept in order to take into account the specific contribution and burdens that the use of connected tools and artificial intelligence could represent for patients. The second is the development of new citizen science methods, in particular via the use of online questionnaires with open questions to massively involve patients in the generation of ideas for research.
He is also co-investigator of the ComPaRe e-cohort, an e-cohort of 50,000 patients suffering from chronic diseases in France.