BMS - Vakgroep OMD (EN)

CV Prof. Irene Klugkist

Irene Klugkist is full professor at the section Methodology and Statistics (M&S), Social and Behavioral Sciences, Utrecht University and professor by special appointment at the Department of Research Methodology, Measurement and Data Analysis (OMD), Behavioral sciences, University of Twente.

Her research interests are Bayesian statistics, evaluation of inequality constrained (a.k.a. informative) hypotheses, accumulation of knowledge through the use of informative priors, and circular data analysis.

The chair of Irene Klugkist at Twente University is Bayesian modelling using informative priors and started in January 2015. Similar research at Utrecht University resulted in the following publications on this topic:

Rietbergen, C., Groenwold, R.H.H., Hoijtink, H.J.A., Moons, K.G.M., Klugkist, I. (in press). Expert Elicitation of Study Weights for Bayesian Analysis and Meta-Analysis. Journal of Mixed Methods Research, DOI: 10.1177/1558689814553850.

Schmidt, A.F., Klugkist, I., Klungel, O.H., Nielen, M., De Boer, A., Hoes, A.W., Groenwold, R.H.H. (in press). Bayesian methods including nonrandomized study data increased the efficiency of postlaunch RCTs. Journal of Clinical Epidemiology. DOI:

De Leeuw, C., Klugkist, I. (2012). Augmenting data with published results in Bayesian linear regression. Multivariate Behavioral Research, 47, 369–391.

Rietbergen, C.,Klugkist, I., Janssen, K.J.M., Moons, K.G.M., Hoijtink, H. (2011). Incorporation of Historical Data in the Analysis of Randomized Therapeutic Trials. Contemporary Clinical Trials, 32, 848-855.

Another research line currently pursued at Utrecht University is the VIDI project A different angle: new tools for circular data for which funding was obtained by Irene Klugkist in 2012 (executed in the period 2013-2018).

At Utrecht University, Irene Klugkist (co-)supervised/supervises the following PhD-students:

  • Fayette Klaassen. Processing within person experimental and longitudinal data using Bayesian updating (expected defence 2019)
  • Kees Mulder. Circular data in experimental and cross-sectional designs. (expected defence 2018)
  • Jolien Cremers. Circular data in longitudinal designs. (expected defence 2018)
  • Haifang Ni. Bayesian techniques to update evidence in veterinary medicine. (expected defence 2018)
  • Leonie van Grootel. Not as we know it: Developing and evaluating synthesis methods that incorporate quantitative and qualitative research (expected defence 2017)
  • Charlotte Rietbergen. Improving epidemiological research by Bayesian analysis (thesis defended February, 12th, 2016)
  • Floryt van Wesel. Model selection in the context of inequality constrained models. (thesis defended July 1st, 2011)
  • Joris Mulder. Inequality constrained models for the multivariate normal mean. (thesis defended December 3rd, 2010) - cum laude

For a full list of academic activities and publications, see the Curriculum Vitae.