PhD Position F/M PhD Offer – 3 years – Oct 2025 to 0ct 2028 Clinical trial designs for assessment of medical devices based on digital twins

Medical digital twins (MDTs) are emerging as personalized decision-support tools in medicine [3]. Examples of DMDs include clinical decision support systems for healthcare professionals, physical devices paired with software like closed-loop insulin pumps [1], apps for monitoring chronic diseases and more. DMDs can offer considerable benefits to patients, healthcare providers, and health systems, and their number continues to grow exponentially [2]. Their complex, interactive, and adaptive nature challenges traditional clinical trial designs, which were primarily developed for drug evaluation. Thus, there is a critical need for new methodological frameworks to assess the efficacy of MDTs in a clinical setting.

This PhD project aims to design statistical methods for evaluating the longitudinal efficacy of MDTs within realistic clinical trial settings. The originality of the thesis lies in the adaptation of advanced statistical tools to compare MDTs with standard of care (SoC), accounting for adaptability and interim analyses.

We will consider a two-arm randomized clinical trial (MDT vs. SoC) with longitudinal outcomes. The thesis will be organized in three work packages:

1. Final efficacy evaluation: formalizing a design to assess the longitudinal effectiveness of the MDT at the end of the trial.
2. Interim analysis and adaptive designs: integrating interim analyses to allow for early stopping or design adaptation (e.g., switching to a better-performing MDT).

A use case considered in this thesis is the use of Interleukin-7 (IL-7) as a complementary intervention to antiretroviral therapy in patients living with HIV with low CD4+ T-cell [4]. Mechanistic models based on ordinary differential equations (ODE) simulate the effect of IL-7 and propose adaptive treatment protocols guided by predictions—representing a concrete MDT strategy. Data of clinical trials for repeated cycle of IL7 are available within the labkey datawarehouse of the SISTM team and will serve as a basis for simulation.

Further application could be exploring how such designs can be adapted for health policy evaluation, such as optimizing vaccination strategies under evolving epidemic conditions.

The candidate will conduct simulation studies using synthetic data to compare the MDT-based approach to standard care in terms of efficacy and efficiency.

Required skills:

• Solid background in statistics and probability
• Experience with longitudinal data analysis and clinical trial methodology
• Proficiency in statistical programming (R, Python, or Julia)
• Familiarity with simulation studies and model-based inference
• Interest in mechanistic models (ODE) and interdisciplinary research in health
• Ability to work independently and in a team
• Strong scientific writing and communication skills in English

Additional appreciated skills:

• Knowledge of Bayesian inference or adaptive trial design
• Familiarity with medical applications or digital health tools

• Subsidized meals
• Partial reimbursement of public transport costs
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

2300€ / month before taxs