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Context: This work is motivated by a study in Type II diabetes patients and their progression to diabetic retinopathy. Specifically, …

Missing values complicate analyses in many studies. Nevertheless, the availability nowadays of methods, such as Multiple Imputation …

Our work is motivated by examples from two large cohort studies, the Generation R Study and the Rotterdam Study, in which the analysis …

Recent Publications


R package

JointAI: Joint Analysis and Imputation of Incomplete Data

[GitHub] [CRAN] [website]

Provides joint analysis and imputation of linear regression models, generalized linear regression models or linear mixed models with incomplete (covariate) data in the Bayesian framework.


R Shiny application

Testing fo the need of nonlinear effects


Online tool to test for non-linear effects in standard regression models (linear, logistic, poisson and Cox regression) using natural cubic splines. The non-linear fit is presented in a graph and the test results are given together with the resulting conclusion.


Together with Prof. Geert Molenberghs, I teach EP16: Missing Data in Clinical Research at the Netherlands Institute of Health Sciences (NIHES)

Materials for the part on multiple imputation can be found on GitHub and the associated website: