Associate professor
Dr. Ir. N. (Nan) van Geloven
Area(s) of expertise:
Biostatistics, Causal inference, Causal prediction
Biostatistics, Causal inference, Causal prediction
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Introduction
As a biostatistician, I specialize in designing and analyzing medical studies, with a particular focus on personalizing and evaluating clinical interventions. The methodological challenges I encounter in my collaborations with medical researchers drive me to improve statistical methods.
I coordinate teaching within the master's program in Statistics and Data Science (Causal Inference I and II) and teach medical and biomedical PhD students ('Statistical Aspects of Clinical Trials' and 'Basic Methods and Reasoning in Biostatistics').
I lead the 'Causal Inference for AI' network between Leiden, Delft, Rotterdam, and Utrecht. I am a member of the Causal Inference Topic Group within the international STRATOS initiative. I am a former member of the Executive Committee of the International Society of Clinical Biostatistics and former editor of Statistica Neerlandica.
I obtained my master in Applied Mathematics at TU Delft, worked and obtained my PhD at the UMCA, where I was chair of the management team of the Clinical Research Unit. Since 2015, I have worked at the LUMC.
I coordinate teaching within the master's program in Statistics and Data Science (Causal Inference I and II) and teach medical and biomedical PhD students ('Statistical Aspects of Clinical Trials' and 'Basic Methods and Reasoning in Biostatistics').
I lead the 'Causal Inference for AI' network between Leiden, Delft, Rotterdam, and Utrecht. I am a member of the Causal Inference Topic Group within the international STRATOS initiative. I am a former member of the Executive Committee of the International Society of Clinical Biostatistics and former editor of Statistica Neerlandica.
I obtained my master in Applied Mathematics at TU Delft, worked and obtained my PhD at the UMCA, where I was chair of the management team of the Clinical Research Unit. Since 2015, I have worked at the LUMC.
Scientific research
My research focuses on causal inference methods and how these can strengthen clinical prediction models. I focus on predicting time to event outcomes while accounting for treatments that change over time. This builds on my earlier work on dynamic prediction models and the ‘prediction estimand’ framework I developed to better incorporate treatment effects into predictions. I also develop methods to evaluate causal predictions.
Currently, I lead a work package on causal model selection within the Safe Causal Inference consortium and a project funded by the Dutch Research Agenda on quantifying uncertainty in clinical decision support. In 2025, I received a personal Vidi grant from ZonMw to study the clinical impact of implementing prediction algorithms in healthcare.
In national and international collaborations with clinical researchers, I apply these methods to research in transfusion medicine, diabetes, fertility, transplantation medicine, and intensive care.
Currently, I lead a work package on causal model selection within the Safe Causal Inference consortium and a project funded by the Dutch Research Agenda on quantifying uncertainty in clinical decision support. In 2025, I received a personal Vidi grant from ZonMw to study the clinical impact of implementing prediction algorithms in healthcare.
In national and international collaborations with clinical researchers, I apply these methods to research in transfusion medicine, diabetes, fertility, transplantation medicine, and intensive care.
