Individualized Predictions, Time-varying Effects and Time-varying Covariates

Extensions of joint models for improving subject-specific predictions.

Multivariate Joint Models

Computational methods for multivariate joint models.

Personalized Active Surveillance and Screening

Novel methods for optimally planning when to collect longitudinal measurements or event information.


The current composition of my research group:

  • Pedro Manuel Miranda Afonso: Pedro is a PhD student working on extensions of joint models for recurrent event data and spatial correlations with applications in cystic fibrosis.

  • Nina van Gerwen: Nina is a PhD student working in causal dynamic predictions using joint models and machine learning.

  • Aglina Lika: Aglina is a PhD student working in developing methodology and software for Bayesian multivariate mixed effects models.

  • Fridtjof Petersen: Fridtjof is a PhD student working in extending joint models to the setting of intensive longitudinal data.

  • Teun Petersen: Teun is a PhD student working in developing new methods for personalized screening with applications in cardiology.

  • Zhenwei Yang: Zhenwei is a PhD student working on applications of joint models in personalized scheduling with applications in prostate cancer research.


The major R packages I have developed and currently maintain


I am the coordinator for the following courses at Erasmus MC:

I have also been teaching short-courses in joint modeling in international conferences. A list of recent courses

Recent & Upcoming Talks

More Talks

Recent Publications

More Publications

  • Optimizing Dynamic Predictions from Joint Models using Super Learning

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  • Sample size calculation for clinical trials analyzed with the meta-analytic-predictive approach

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  • Modeling the underlying biological processes in Alzheimer's disease using a multivariate competing risk joint model

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  • Shared decision making of burdensome surveillance tests using personalized schedules and their burden and benefit

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  • Incorporating historical controls in clinical trials with longitudinal outcomes using the modified power prior

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  • JointAI: Joint analysis and imputation of incomplete data in R

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  • An alternative characterization of Missing at Random in Shared Parameter Models for incomplete longitudinal data and its utilization for sensitivity analysis

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  • Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model

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  • A Bayesian joint model for zero-inflated integers and left-truncated event times with a time-varying association: Applications to senior health care

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  • A marginal estimate for the overall treatment effect on a survival outcome within the joint modeling framework

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I have written the first book on Joint Models for Longitudinal and Survival Data


Inaugural Speech

A trailer of my inaugural address is available here.


A full list of my publications and grants can be found in my CV.