Randomized clinical trials are widely accepted as the gold standard for assessing the safety and efficacy of new medical interventions. It is not always feasible to recruit the number of patients required for a fully powered clinical trial, and therefore, in some cases, the data of a clinical trial may be augmented by external data sources. Such external data may come from a previous clinical trial or from non-experimental studies and data sources. In recent years a variety of Bayesian statistical methods have been developed that enable this borrowing of information from external data, while providing safeguards against the effects of possible differences between data sources.
The project's primary objective is to enhance the understanding of methodological issues related to the use of external data in clinical trials. The project involves identifying practical and methodological barriers to implementation and conducting pertinent research to eliminate these obstacles. This encompasses refining theoretical aspects such as type I error and statistical power, exploring regulatory agencies' acceptability criteria, developing applicable software, and applying existing dynamic borrowing methods in practical research. The project is funded through a public-private partnership and performed in collaboration with the Danone Nutricia Research & Innovation (R&I) Center, located in Utrecht. You will work with datasets provided by Danone and focus on novel methodologies and applications that are directly driven by Danone’s research needs in the field of innovative nutritional science.

The outcomes of the PhD project will include publications in peer-reviewed statistical journals. Additionally, there is the possibility of creating statistical software tailored to the project's needs. You will also share your findings through internal and external presentations, including presentations at international conferences, and through engagements with stakeholders. Additionally, you will actively contribute to the institute's academic environment by participating in seminars, workshops, and collaborative research.

Your daily worksite is the Department of Data Science & Biostatistics at Julius Center of UMC Utrecht. You will also be expected to work regularly at Danone Nutricia Research, conveniently located just across the street of the UMC Utrecht. The project will be supervised by Dr. Joost van Rosmalen (UMC Utrecht), Dr. Maarten van Smeden (UMC Utrecht) and Dr. Floor van Oudenhoven (Danone Nutricia Research).

• A Master’s degree in (bio)statistics, data science, econometrics, mathematics or equivalent.
• Strong analytical skills and an interest in methodological research for clinical trials.
• The ideal candidate is self-motivated, with strong time management skills.
• Strong communication abilities are essential for collaborating effectively with peers and supervisors.
• A proactive attitude and the ability to collaborate effectively across scientific disciplines and with the industrial partner.
• The candidate should have the ability to explain the relevance of statistical and methodological considerations to commercial and regulatory stakeholders.
• The candidate ideally has knowledge of / experience with the following:
  • Bayesian and frequentist statistical methods
  • Design and statistical analysis of clinical trials
  • Proficiency in scientific English
  • The ability to program in R or experience with another programming language with an interest to learn R.
  • Knowledge of or experience with medical research is considered an asset.

• A salary between € 3017 en € 3824 gross per month (salary scale OIO), based on full-time employment (36 hours).
• Salary increase of 3% from July 2025, according to the collective labor agreement (cao).
• Year-end bonus of 8.3% and holiday allowance of 8%.
• Pension insurance with ABP. We take care of approximately 70% of the monthly contribution.
• 100% public transport reimbursement. Are you coming on foot, by bike or by car? You will then receive a reimbursement of € 0.18 per km (by car up to a max. of 40 km one way).
• Possibilities to develop yourself personally and professionally.
• The option to select additional employment benefits in exchange for gross salary, such as purchasing a bicycle and memberships.