About

The R&E group Biostatistics significantly contributes to improving health and healthcare based on research design, modelling and data analysis firmly grounded in statistics and mathematics. It supports developing new treatments for rare diseases, innovation in drug regulatory science, modelling dynamics of infectious diseases and advancing novel data analytical approaches to clinical practice (data driven innovation). 

Scope and area of work

The R&E group biostatistics holds a strong research portfolio focussing on application of biostatistics to health and health care related problems that are of high interest for patients and society. 

 

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Scope and area of work

The R&E group biostatistics includes a strong research portfolio, engages in education from BSc, Msc to post-academic level and includes an internal consultancy service for researchers and PhD students. Across the portfolio the R&E group focuses on application of biostatistics to health and health care related problems that are of high interest for patients and society. This ranges from enabling new treatments being developed for highly unmet needs (e.g., rare diseases), to methods to assess complex prediction models for use in clinical practice to improve individualised treatment. The research is covered in the following themes:

  1. Clinical trials & Real World Data for Regulatory Science.
  2. Innovative clinical trials for rare diseases.
  3. Advanced modelling of longitudinal and survival data, including federated inference.
  4. Models and methods for infectious diseases.
  5. Statistical inference and use of AI in practice.

Clinical trials & Real World Data for Regulatory Science.
Our R&E group has a strong research track record improving methodology for clinical trials, ranging from cluster randomised trials for complex health interventions to subgroup analyses in trials, hybrid Bayesian – frequentist approaches and multi-arm trials such as basket and umbrella designs. There is particular focus on drug development and regulatory decision making, with long standing collaboration with CBG-MEB and the EMA. A second core of research in this context is the use of Real World Data to augment the evidence from randomised trials. Hybrid designs, use of Real World Data to establish drug effects in target populations and use of real world data for extrapolation of trial results to broader populations are key themes. 

Innovative clinical trials for rare diseases.
As part of the Radboudumc’s concerted effort to bring novel treatment options to rare disease patients, this research theme focuses on trial designs and analyses that meet the challenge that the numbers of patients that can engage in clinical trials are small. Methodologies cover a broad range: Leveraging mechanistic modelling and extrapolation for efficient trials, augmenting trials with external control data, basket trials across mechanistically similar diseases for repurposed drugs, n-of-1 designs and Bayesian approaches. It also includes improving and validating novel outcomes (such as Goal Attainment Scaling) that can deal with the heterogeneity between patients that is also typical for many rare diseases.

Advanced modelling for longitudinal and survival data, including federated inference.
Medical research questions often involve complex modelling of survival or longitudinal data. The R&E group Biostatistics has key expertise and research in these areas with broad potential for application: from clinical trials, observational data to public health data. Research and expertise incudes novel methods that can efficiently apply complex modelling with federated data: Data is available at different locations, but they cannot be merged to one dataset due to legal and practical limitations. Bayesian federated inference as developed by our group and the Radboud University Faculty of Science can construct results from local inferences in separate centres what would have been inferred had the datasets been merged. Especially if datasets are small across multiple countries, this has huge practical advantages.

Models and methods for infectious diseases.
Our main aim is to advance the understanding of infectious diseases through mathematical and statistical models. In particular, our group focuses on developing innovative approaches to address the multifaceted challenges posed by infectious diseases within clinical and epidemiological contexts. Research encompasses a wide range of infectious diseases including both directly transmitted pathogens (e.g. SARS-CoV-2) and vector-borne diseases (e.g. malaria, schistosomiasis). Specific activities are transmission models and scenario analysis to estimate the impact of control interventions, longitudinal and survival models, Bayesian spatial and latent variable models.

Emerging theme: Statistical inference and use of AI in practice.
Pathologists are exploring the potential of Artificial Intelligence (AI) to enhance breast cancer treatment decisions. AI-driven software can accurately extract vital information from images, enabling predictions about disease recurrence and treatment outcomes. In a new collaborative research project, i.e. COMMITMENT project, funded by the KWF Dutch Cancer Society, and led by Jeroen van der Laak and Francesco Ciompi, researchers aim to apply this AI technology to benefit three distinct groups of breast cancer patients. Our group’s contribution focuses on the validity and reliability of the translation of the AI solutions for clinical practice. It is a first project in its kind, but it is estimated this will be a topic of increasing interest for biostatistical contributions. 


Chairs/RGL's

View chairs and research group leaders (RGLs) within this group.

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Chairs/RGL's

The R&E group Biostatistics is headed by prof. dr. Kit Roes, who holds the chair in Biostatistics (link will follow).



Projects

View our projects and ongoing research.

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Projects




Initial education

Biostatistics is core for medicine and biomedical sciences Bsc students and the group engages thoughout the Bsc phase. At Msc level we offer several courses on methodology, close to our key focus areas: clinical trials, survival and longitudinal data analyses, experimental design and causal inference. These are either part of the Biomedical Sciences master program at Radboudumc (read more).




Post-initial education

It is also possible to follow post-academic courses, especially 'Statistics for clinical researchers' to be well prepared for research projects. More specialized courses (e.g., in survival or longitudinal analyses or Bayesian methods) are also provided, depending on demand.




Collaborations

Long standing collaborations include the Dutch Medicines Evaluation Board (College ter Beoordeling van Geneesmiddelen – CBG-MEB), the European Medicines Agency (EMA), the Faculty of Science of Radboud University and Erasmus Medical Center (Prof. van der Vlas) and Oslo University. Through the portfolio of (international) projects, collaborations included are (a.o.) University Medical Center Groningen, Inserm, Karolinska Institute and Hospital, Hannover Medical School, University of Lisbon, University Medical Center Utrecht and Utrecht University. The group also participates in ERDERA (European Rare Disease Research Alliance).




Consultancy

The R&E group Biostatistics engages in the Radboudumc Research Technology Center Biostatistics and Health Economics. The RTC offers internal consultancy to researchers and PhD students within or associated with the Radboudumc. The main objective of this service is to enhance the quality and integrity of Radboudumc research. It is therefore very accessible and promoted to engage early – at research design and/or research proposal stage.

If you are interested in consultations or services from an R&E group, please fill out our application form.


Team members