Recent & Upcoming Talks

Missing values pose a complication many applied researchers need to deal with, however, the handling of missing values is usually not …

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

Projects

Joint Analysis and Imputation of Incomplete Data in R

R package JointAI for analysis of incomplete data in the Bayesian framework.

Software

R package

JointAI: Joint Analysis and Imputation of Incomplete Data

[GitHub] [CRAN] [website] [project]

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.

Vignettes:


R Shiny application

Testing fo the need of nonlinear effects

[link]

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.

Teaching

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:


FGME 2019

On 15 september 2019 I teach the pre-conference course on Multiple Imputation of Missing Data in Simple and More Complex Settings at the Tagung der Fachgruppe Methoden & Evaluation der Deutschen Gesellschaft für Psychologie in Kiel:

  • Slides: (version 2019-09-13) [view] [download]
    Handout version of the slides: [download]

  • Practicals: (version 2019-09-13)

    • Multiple Imputation with the mice package [view]
    • Evaluating and checking the imputation [view]
    • Imputation with non-linear associations [view]
    • Imputation with multi-level data [view]
    • .zip with all materials [download]

For the practicals, please download and install

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