About our department
- Longitudinal Data Analysis & Hierarchical Modeling
- Survival Analysis
- Joint Modeling of Longitudinal and Time-to-Event Data
- Statistical Analysis with Missing Data
- Modern Analysis of Clinical Trials
- Bioinformatics and Statistical Genetics
- Growth Curves
- Smoothing Techniques
- Bayesian Modeling
Our Staff Assistant:
Ms. Eline van Gent
Biostatistics Secretarial Office
Absent on Wednesday
dr. E.R. (Elrozy) Andrinopoulou, PhD
dr. S.J. (Sara) Baart
dr. N.S. (Nicole) Erler, PhD
Prof. dr. B.E. (Bettina) Hansen
Professor of Clinical Biostatistics
dr. J.M. (Joost) van Rosmalen, PhD
Assistant professor in biostatistics
dr. S.P. (Sten) Willemsen, PhD
Individualized Dynamic Predictions:
Individualized predictions play a key role in precision medicine and shared decision making. Joint models for longitudinal and survival data have been shown to be a valuable tool in this context. In this research line we study and explore different types of extensions of joint models that can improve the quality of the derived predictions.
Personalized Active Surveillance and Screening
Decision making in medicine has become increasingly complex for patients and practitioners. This has resulted from factors such as the shift away from physician authority toward shared decision making, unfiltered information on the Internet, new technology providing additional data, numerous treatment options with associated risks and benefits, and results from new clinical studies. Within this context medical screening procedures are routinely performed for several diseases. In general, the aim of screening procedures is to optimize the benefits, i.e., early detection of disease or deterioration of the condition of a patient, while also balancing the respective costs.
In this research line we develop novel techniques for optimally choosing when to collect biomarker information for patients in a screening phase, and when to plan an invasive procedure. The key element of these techniques is their personalized and dynamic nature, i.e., they suitably adapt utilizing the available information on a patient.
Statistical Analysis with Missing Data
The statistical analysis of almost any type of data collected in human health research is complicated from incomplete information. Even though researchers would like to obtain specific measurements from the study participants, very often this information is missing. In this research line we develop new statistical analysis techniques that allow to make the optimal use of the available data and derive the most useful and relevant conclusions.
Novel Analysis of Clinical Trials
Clinical trials are the primary tool for evaluating the efficacy and safety of new medications and procedures. However, to achieve these results clinical trial typically require enrolling many patients. In this research line we develop novel methodology for analyzing clinical trials using information from previous studies, and hence decreasing the required number of patients to be enrolled.
Head Department of Biostatistics
Prof.dr. Dimitris Rizopoulos
Dr Sara Baart
Dr Nicole Erler
Prof.dr. Bettina Hansen
Dr. Wim Hop