L-BioStat is a research unit within the Department of Public Health and Primary Care at the KU Leuven Medical Faculty, specializing in methodological research on hierarchical models, missing data, surrogate endpoint evaluation, epidemiology, and spatial statistics, among other areas. L-BioStat is a partner of the Leuven Statistics Research Centre (LStat) and the Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat). Its staff members have published extensively in both methodological and applied journals and hold positions in international professional societies such as the ASA, RSS, ISCB, and IBS.
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Project
Background: Osteoarthritis (OA) is a degenerative joint disease increasingly prevalent worldwide, especially among older adults, due to factors like aging and rising obesity rates. Knee OA, the most common form, affects millions, causing pain, reduced mobility, and diminished quality of life. This growing health burden often necessitates long-term management or surgical interventions, such as total knee replacement. Traditional clinical trials for OA treatments require extensive follow-up periods to measure clinically relevant outcomes, delaying the introduction of new therapies. Consequently, there is a strong demand for alternative methods that can accelerate trial timelines and reduce costs, especially for disease-modifying treatments that aim to alter the disease’s progression rather than just manage symptoms.
A promising approach to accelerating clinical evaluations is the use of surrogate endpoints, such as measurements of cartilage thickness (CT) or synovitis, which allow for earlier assessment of treatment efficacy. Surrogate endpoints are intended to act as reliable substitutes for the most relevant clinical outcome, known as the true endpoint, in clinical trials. However, for regulatory approval, it is essential to demonstrate that the treatment effect on these surrogate endpoints reliably predicts real clinical benefits. This requiers the development of statistical methodologies to rigorously assess potential surrogate endpoints.
Objectives: The project will focus on developing and validating statistical models to assess surrogate endpoints in knee OA, within the meta-analytic and Information-Theoretic Causal-Inference frameworks. Additionally, the project aims to establish surrogacy metrics with appropriate mathematical properties and clinically meaningful interpretations tailored to the OA context, ensuring these metrics meet regulatory standards for predictive accuracy. The proposed models and surrogacy metrics will be refined and validated using data from established resources, including the Osteoarthritis Initiative (OAI) database and randomized controlled trials (RCTs), along with simulation studies. Another key objective is to evaluate specific candidate surrogate endpoints for knee OA, with a particular focus on synovitis, which has shown potential as an indicator of OA progression and treatment efficacy in preliminary studies.
Impact: Identifying valid surrogate endpoints for knee OA could transform clinical trials, enabling faster and more cost-effective evaluation of new therapies. This would allow patients quicker access to innovative treatments with the potential to significantly improve their quality of life. This research also aims to advance statistical methods for validating surrogate endpoints, creating a framework to improve the efficiency of clinical trials across medical fields and support faster development of effective treatments for chronic conditions.
Profile
- Master degree in quantitative sciences (statistics, mathematics, physics, etc.).
- Extensive knowledge of statistics.
- Good presentation skills.
- Interests in developing new statistical methodology to address clinically relevant research questions.
- Good prior study results.
- Proficiency in English (orally and written).
Offer
Employment will be in an interesting team where research is conducted which is highly relevant for the general public.
Interested?
For more information please contact Prof. dr. Ariel Alonso Abad, tel.: +32 16 32 58 44, mail: ariel.alonsoabad@kuleuven.be or Prof. dr. Geert Molenberghs, tel.: +32 16 37 33 11, mail: geert.molenberghs@kuleuven.be.
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