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Department

Biostatistics

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.

Projects

Notable results

Publications

 

Head Department of Biostatistics

Prof.dr. Dimitris Rizopoulos

 

Staff

Prof.dr.Lidia Arends 
Dr Elrozy Andrinopoulou
Dr Nicole Erler
Dr Joost van Rosmalen
Dr Sten Willemsen

 

PostDoc

Dr Sara Baart
Dr Anja Rüten-Budde

 

PhD

Anirudh Tomer
Pedro Afonso
Greg Papageorgiou
Hongchao Qi

 

Emeriti Biostatistics

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










 

 

Facilities

SPSS
Latex
R
 

Collaborations

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

Events

International Biometric Society (IBC)

International Society for Bayesian Analysis (ISBA)

International Society for Clinical Biostatistics (ISCB)

Joint Statistical Meetings (JSM)

The World of Statistics Activities Calendar
 

Awards and grants

Awards Department of Biostatistics

Year

Name

Conference

Award

Presentation / Poster

2012

Magdalena Murawska

ISCB 33rd

Student Conference Award

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

2012

Eleni Rosalina Andrinopoulou

IWSM 27th

Extraordinary Student Oral Presentaion

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

2014

Eleni Rosalina Andrinopoulou

SAM 2nd

Poster Award

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

2014

Eleni Rosalina Andrinopoulou

ISCB 35rd

Student Confrence Award

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

2015

Kazem Nasserinejad

EMR - IBS 8th

EMR Student Schlarship

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

2015

Nicole Erler

EMR - IBS 8th

Student Conference Award

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

2016

Nicole Erler

ISCB 37th

Student Conference Award

Bayesian imputation of time-varying covariates in linear mixed models

2018

Anirudh Tomer

EMR - IBS 10th

Student Conference Award

Personalized schedules for surveillance of low risk prostate cancer patients

2018

Anirudh Tomer

IBC 2018

2nd Best oral speaker

Personalized schedules for surveillance of low risk prostate cancer patients

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