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The department of Biostatistics develops novel statistical models and procedures that are motivated and applied in clinical, epidemiological and public health research and practice.

About our department

Our research

The current areas of active research of the department cover the following topics:

- 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

Principal Investigators

Research Lines

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.


CPO-projects; Biostatistical Consultancy 
CPO stands for "Consultation centre for Patient Oriented research"

Dissertations & Books



Head Department of Biostatistics

Prof.dr. Dimitris Rizopoulos



Prof.dr.Lidia Arends 
Dr Elrozy Andrinopoulou
Dr Nicole Erler
Dr Joost van Rosmalen
Dr Sten Willemsen
Dr Sara Baart
Dr. Bettina Hansen


Emeriti Biostatistics

Prof.dr.Emmanuel Lesaffre
Prof.dr.ing.Paul Eilers
Dr Wim Hop




News, events and awards

Our news

6 September 2019,  Inaugural Lecture - prof. dr. Dimitris Rizopoulos

1 April 2019, dr. E.R. Andrinopoulou Promoted to Assistant Professor


International Biometric Society (IBC)

International Society for Bayesian Analysis (ISBA)

International Society for Clinical Biostatistics (ISCB)

Joint Statistical Meetings (JSM)


Awards and grants

Awards Department of Biostatistics





Presentation / Poster


Magdalena Murawska

ISCB 33rd

Student Conference Award

Dynamic Prediction Based on Joint Model for Categorical Response and Time-to-Event


Eleni Rosalina Andrinopoulou

IWSM 27th

Extraordinary Student Oral Presentaion

Joint Modeling of Two Longitudinal Outcomes and Competing Risk Data. An Application in Cardio Data.


Eleni Rosalina Andrinopoulou

SAM 2nd

Poster Award

Combined Dynamic Predictions Using Joint Models of Multiple Longitudinal Outcomes and Competing Risk Data


Eleni Rosalina Andrinopoulou

ISCB 35rd

Student Confrence Award

Combined Dynamic Predictions Using Joint Models of Multiple Longitudinal Outcomes and Competing Risk Data


Kazem Nasserinejad

EMR - IBS 8th

EMR Student Schlarship

Latent Class Mixed-Effects Transition Model: A model to predict hemoglobin in blood donors


Nicole Erler

EMR - IBS 8th

Student Conference Award

Missing Covariates in Epidemiologic Studies: MI vs. a Full Bayesian Approach


Nicole Erler

ISCB 37th

Student Conference Award

Bayesian imputation of time-varying covariates in linear mixed models


Anirudh Tomer

EMR - IBS 10th

Student Conference Award

Personalized schedules for surveillance of low risk prostate cancer patients


Anirudh Tomer

IBC 2018

2nd Best oral speaker

Personalized schedules for surveillance of low risk prostate cancer patients

Any questions?

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