Academic Center for Quantitative Methods

To promote both the development and application of sound and innovative quantitative methods for all research activities within the Erasmus MC.

Academic Center of Excellence

Research Activities

The ACE-QM is at the forefront of methodological research with clear motivations coming from clinical and epidemiological practice. The research projects of the center combine new developments in statistical, epidemiological and decision making methodology that make substantial contributions to the fields that motivated the research questions. Some highlights of the current research projects are:


* Development of methodology for optimal personalized screening intervals for biomarkers. The motivating question behind this project is: How can we then plan the timing of the next biomarker measurement, so that we will maximize the information we can gain on disease progression, while minimizing costs and patient burden?


* Development of dynamic diagnostic prediction models with the goal of choosing diagnostic procedures more effectively.


* Development of sophisticated clinical trial designs to efficiently and effectively evaluate new diagnostic and therapeutic procedures.


* Characterization of diseases and comparative effectiveness research.


* Development of methods for calibration and validation of models, including prediction models, decision models and health technology assessment models.


Type of


Sophisticated clinical trial designs: Collaboration with the departments of Radiology, Cardiology and Surgery of the Erasmus MC

Dynamic diagnostic prognostic models: Collaboration with the departments of Radiology, Neurology, Cardiology, and Revalidation of Erasmus MC



The center is responsible for providing quantitative education at three levels:


1st Level: Teaching epidemiology and statistics courses in the medical curriculum, Clinical Technology, and Erasmus University College at the bachelor and master levels mainly led by the groups of Biostatistics and Clinical Epidemiology.


2nd Level: The groups of Biostatistics, Medical Decision Making and Clinical Epidemiology actively contribute to the statistics, decision making, and clinical research courses in master programs, specifically within the NIHES graduate school.


3rd Level: Post-graduate education in the form of seminars, lectures, master classes and short courses for PhD students, post-docs, residents, and faculty.

The faculty of the ACE-QM receives every year exemplary evaluations on these teaching activities. In addition, the center actively contributes in new educational initiatives, such as bringing use of ICT in teaching, and e-Learning. The students in these courses come from all over the world: we have students from all over the Netherlands, both Western and Eastern European countries, Asia, South America, North America, and even Australia in our NIHES courses. In addition, several of our teaching activities take place elsewhere (including Harvard TH Chan School of Public Health) demonstrating our excellent international reputation in education.


Care Activities

The ACE-QM is not directly related to patient care. However, better quantitative methods related to comparative effectiveness and efficiency research lead to better evidence that will be implemented in patient care in the mid to long term, in collaboration with the clinical partners involved.

Societal Relevance to Research, Education and Patient Care

The ACE-QM actively contributes in valorization of sound methodology. A prime example is the active participation in the "STRengthening Analytical Thinking for Observational Studies: The STRATOS initiative" (


The objective of STRATOS is to provide accessible and accurate guidance in the design and analysis of observational studies. In addition, novel statistical methods developed by members of the center have been put into practice in several clinical fields, including among others, thorax surgery, radiology, prostate cancer, neurology, surgical oncology and cardiology. The methods developed in our ACE are very relevant to medical decision making and health technology assessment, which in turn has enormous relevance to policy making in health care and implications for society as a whole.


In addition, the ACE-QM is actively involved in the development of software that implements sophisticated statistical procedures. These procedures and software will be made readily available to phycisians by integrating in the GemsTracker software package that is currently in clinical practice.

Viability of Research, Education and Patient Care

The feasibility of research and education constitutes on the primary objectives of the ACE-QM. There are many activities (internal seminars, etc.) that aim to keep both junior and senior members of the center up to date with current research and educational practices. All PhD students actively participate in international conferences, by giving talks and also following short course offered by worldwide experts.


These activities have helped many of them in establishing networks that help them in their future career plans. The senior members of the center also actively participate in several international conferences, giving invited lectures and short courses themselves. This has helped in identifying international talents and enticing some of them in doing a PhD in Erasmus MC. Furthermore, many members of the center are active in social media (Twitter, GitHub, blogging) that help disseminate their research output.

Key and relevant publications of the last five years

  • Dhana, K., van Rosmalen, J., Vistisen, D., Ikram, M.A., Hofman, A. Oscar, F.H. and Kavousi, M. (2016). Trajectories of body mass index before the diagnosis of cardiovascular disease: a latent class trajectory analysis. European Journal of Epidemiology 31, 583-592.
  • Brankovic, M., Kardys, I., Hoorn, E.J., Baart, S., Boersma, E. and Rizopoulos, D. (2018). Personalized dynamic risk assessment in nephrology is a next step in prognostic research. Kidney International 94, 214-217.
  • Andrinopoulou, E.R., Eilers, P.H.C., Takkenberg, J.J.M. and Rizopoulos, D. (2018). Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines. Biometrics 74, 685-693.
  • Kardys I, Baart S, van Domburg R, Lenzen M, Hoeks S, Boersma E. Tools and Techniques - Statistical: A brief non-statistician's guide for choosing the appropriate regression analysis, with special attention to correlated data and repeated measurements. EuroIntervention. 2015 Dec;11(8):957-62.
  • Nieboer D, Vergouwe Y, Roobol MJ, Ankerst DP, Kattan MW, Vickers AJ, Steyerberg EW; Prostate Biopsy Collaborative Group. Nonlinear modeling was applied thoughtfully for risk prediction: the Prostate Biopsy Collaborative Group. J Clin Epidemiol. 2015 Apr;68(4):426-34
  • van Kempen BJH, Ferket BS, Steyerberg EW, Max W, Hunink MGM, Fleischmann KE. Comparing the cost-effectiveness of four novel risk markers for screening asymptomatic individuals to prevent cardiovascular disease (CVD) in the US population. Int J Cardiol 2016 Jan 15;203:422-31.
  • Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ. 2016 Jan 25;352:i6.
  • Genders TS, Petersen SE, Pugliese F, Dastidar AG, Fleischmann KE, Nieman K, Hunink MG. The optimal imaging strategy for patients with stable chest pain: a cost-effectiveness analysis. Ann Intern Med. 2015 Apr 7;162(7):474-84.
  • van Klaveren D, Gönen M, Steyerberg EW, Vergouwe Y. A new concordance measure for risk prediction models in external validation settings. Stat Med. 2016 Jun 1.
  • van Kempen BJ, Ferket BS, Kavousi M, Leening MJ, Steyerberg EW, Ikram MA,Witteman JC, Hofman A, Franco OH, Hunink MG. Performance of Framingham cardiovascular disease (CVD) predictions in the Rotterdam Study taking into account competing risks and disentangling CVD into coronary heart disease (CHD) and stroke. Int J Cardiol. 2014 Feb 15;171(3):413-8.
  • Nasserinejad, K., van Rosmalen, J., de Kort, W., Rizopoulos, D. and Lesaffre, E. (2016). Prediction of hemoglobin in blood donors using a latent class mixed-effects transition model. Statistics in Medicine 35, 581-594.
  • Rizopoulos, D., Hatfield, L., Carlin, B. and Takkenberg, J. (2014). Combining dynamic predictions from joint models for longitudinal and time-to-event data using Bayesian model averaging. Journal of the American Statistical Association 109, 1385-1397.
  • Rizopoulos, D., Taylor, J.M.G., van Rosmalen, J., Steyerberg, E.W. and Takkenberg, J.J.M. (2016). Personalized screening intervals for biomarkers using joint models for longitudinal and survival data. Biostatistics 17, 149-164.

PhD theses of the last five years

  • Joint Modeling of longitudinal and survival data with application in Heart Valve data. Andrinopoulou, 2014
  • Bayesian variable selection in high-dimensional applications. Rockova, 2013
  • Multilevel regression models for mean and covariance, with applications in nursing research. Li, 2014
  • Diagnostic imaging strategies for patients with suspected coronary artery disease. Genders 2012
  • Genetic risk prediction for common diseases. Methodology and applications. Mihaescu 2013
  • Personalized medical decision making for prevention of a first cardiovascular event. Ferket 2013
  • Cost-effectiveness of primary prevention strategies for cardiovascular disease. Van Kempen 2016

Non-scientific publications related to the ACE

Principal coordinator(s)

Collaborating investigator(s)

Last updated: 365 days ago.