Personalized medicine aims to match individuals with the therapy that is best suited to them and their condition. Based on large-scale medical “Big” data, predictive approaches to treatment effect heterogeneity (PATH) are based on absolute disease probabilities, and their differences, to best support clinical decision making. Enrichment designs have been proposed to target relevant subpopulations of patients in phase II or III clinical trials with the aim to optimize drug development using adaptive designs allowing to pursue drug evaluation in specific subgroups only when deemed promising. By contrast, such enriched approaches have been scarcely used in dose-finding trials, while the initial determination of an optimal dose is crucial for further development of a drug in a population of interest.
The candidate will be based in the team ECSTRRA (Epidemiology and Clinical Statistics for Tumor, Respiratory, and Resuscitation) of INSERM U1153, located at the Department of Biostatistics of Saint Louis hospital in Paris (https://www.sbim-stlouis.org/), France where they will benefit from real-life experience working with specialists on clinical trials, including early phase trials and causal inference.
The objectives of the project are to:
- (a) Review existing literature of the design and analysis approaches of enrichment dose-finding trials.
- (b) Develop new approaches motivated by ongoing early phase trials conducted at Saint Louis hospital to assess methodological issues, such as the ordering across heterogeneity subgroups or strata, sharing information between strata, joint evaluation of safety and efficacy, decision criteria using Bayesian methods.
- (c) Test statistical properties of selected trial designs and analysis strategies
- (d) Illustrate the newly developed efficient designs on case studies (post hoc analyses based on existing trial datasets) or influence the methodologies of future such trials.
At least 2 peer reviewed publications are anticipated in a methodological/clinical trial journal, from this project.
This project is part of the SMATCH consortium “Statistical and AI based Methods for Advanced Clinical Trial CHallenges in Digital Health” of the national program PEPR Santé Numérique, Programme #1 (https://pepr-santenum.fr/). The supervisory team for this project includes Dr Lucie Biard, Dr Moreno Ursino and Prof Sylvie Chevret. The project will include consulting with other members of the SMATCH consortium group to design and plan the study, to present results.
The position (PhD or post-doc) will be defined according to the candidate. In case of a PhD program, the position will be a full-time 36-months PhD contract and the student will be affiliated to Graduate School 393 Pierre Louis de Santé Publique – Université Paris Cité – Sorbonne Université (https://ed393.sorbonne-universite.fr/lecole-doctorale-393). In case of a post-doc, and the exact duration of the contract will depend on the candidate’s background and previous experience, and be 36 months at most.
Requirements: Master or PhD level in biostatistics, clinical epidemiology, applied mathematics, with experience and proficiency in R or Python programming.
Deadline for application: The position is available immediately and will remain open until a suitable candidate is selected.
Start date: Fall 2024 (academic year 2024-25).
Duration: 24 to 36 months (depending on entry level)
Salary: Doctoral or postdoctoral level; INSERM salary scale.
To apply, please send your CV and cover letter to lucie.biard@u-paris.fr, moreno.ursino@inria.fr, and sylvie.chevret@u-paris.fr
