Summer Sale

Up to 30% off — ends 7 Aug

ONLY00h00m00s

Get 30% Off

Up to 30% off — ends 7 Aug

ONLY00h00m00s

Jeroen Hoogland

Assistant professor

Amsterdam UMC

Netherlands

This profile is automatically generated from trusted academic sources.

Google Scholar

ORCID

How do I reach out?

Meet Kite AI

Contact this professor

ORCID

Google Scholar

Research Interests

Biostatistics

70%

Epidemiology

30%

Predictive Modeling

40%

Cohort Study

40%

Clinical Prediction

40%

Pilot Study

30%

Outcome Prediction

30%

Ask ApplyKite AI

Start chatting

How can you help me contact this professor?

What are this professor's research interests?

How should I write an email to this professor?

Articles15

Alternatives to default shrinkage methods can improve prediction accuracy, calibration, and coverage: A methods comparison study

Article Link

Year:

2025

Measuring the Performance of Survival Models to Personalize Treatment Choices

Article Link

Year:

2025

Risk‐Based Decision Making: Estimands for Sequential Prediction Under Interventions

Article Link

Year:

2024

Evaluating individualized treatment effect predictions: A model‐based perspective on discrimination and calibration assessment

Article Link

Year:

2024

Measuring the performance of prediction models to personalize treatment choice

Article Link

Year:

2023

Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis

Article Link

Year:

2023

Propensity‐based standardization to enhance the validation and interpretation of prediction model discrimination for a target population

Article Link

Year:

2023

The relation between prediction model performance measures and patient selection outcomes for proton therapy in head and neck cancer

Article Link

Year:

2023

Regularized parametric survival modeling to improve risk prediction models

Article Link

Year:

2023

Diagnostic prediction models for CT-confirmed and bacterial rhinosinusitis in primary care: individual participant data meta-analysis

Article Link

Year:

2022

Diagnostic prediction models for CT-confirmed and bacterial rhinosinusitis in primary care: Individual participant data meta-analysis

Article Link

Year:

2022

Accuracy of approximations to recover incompletely reported logistic regression models depended on other available information

Article Link

Year:

2022

Predicting personalised absolute treatment effects in individual participant data meta‐analysis: An introduction to splines

Article Link

Year:

2022

A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Article Link

Year:

2021

Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling

Article Link

Year:

2021

Collaborators13

Thijs van Ommen

Utrecht University

NETHERLANDS

Matthias Schwenkglenks

University of Zurich

SWITZERLAND

Chris van Lieshout

Assistant Professor

Universitair Medisch Centrum Utrecht

NETHERLANDS

Orestis Efthimiou

SWITZERLAND

Joanna IntHout

NETHERLANDS

Richard D Riley

Professor of Biostatistics

University of Birmingham

UNITED KINGDOM

Ian White

Professor at Institute of Clinical Trials and Methodology (ICTM)

University College London

UNITED KINGDOM

Roderick P Venekamp

Associate Professor

University Medical Center Groningen

NETHERLANDS

Michael Seo

University of Bern

SWITZERLAND

Frank Harrell

Professor

Vanderbilt University Medical Center

UNITED STATES

A. H. Zwinderman

NETHERLANDS

Thomas P. A. Debray

NETHERLANDS

Niels Peek

Professor of Data Science and Healthcare Improvement

University of Cambridge

UNITED KINGDOM

How do I reach out?

Meet Kite AI