Nicole Erler

Assistant Professor Biostatistics

University Medical Center Utrecht


I am an Assistant Professor at the Department of Data Science and Biostatistics at the Julius Center for Health Sciences and Primary Care, UMC Utrecht, the Netherlands. My research interests are the development of methodology to personalize health care, for example, via personalizing screening and intervention schedules, and methods to deal with incomplete data in complex settings. I work with techniques such as joint and longitudinal modelling, dynamic prediction, and the Bayesian framework.


  • Statistical Methods for Missing Data
  • Bayesian Methods
  • Longitudinal Data Analysis
  • Joint Modelling
  • Dynamic Prediction


  • PhD in Biostatistics, 2019

    Erasmus Medical Center, Rotterdam, the Netherlands

  • Doctor of Science in Epidemiology, 2014

    Netherlands Institute of Health Science, Rotterdam, the Netherlands

  • Diplom Statistics (equivalent BSc + Msc), 2012

    Ludwig-Maximilians-Universität München, Munich, Germany

Recent & Upcoming Talks

Dynamic Prediction

Imputation Magic - How (not) to deal with incomplete data

When and Why Imputation with MICE / FCS Might Fail

Working with Missing Data and Imputation

Dealing with Missing Values in Multivariate Joint Models for Longitudinal and Survival Data

Recent Publications


Joint Analysis and Imputation of Incomplete Data in R

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


R package JointAI

R package for Joint Analysis and Imputation of incomplete data in R using the Bayesian framework

Shiny application: Non-linear effects

R shiny application to test for the need of non-linear effects using splines in linear, logistic, poisson and Cox regression models