B. Van calster

Statistics and Probability · 2012–2021

A researcher in the Science corpus: 8 papers; trusts 12 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 57 citations into it
• Artificial Intelligence — 55 citations into it
• Economics and Econometrics — 25 citations into it
• Pulmonary and Respiratory Medicine — 12 citations into it
• Health Information Management — 9 citations into it

Whom B. Van calster trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ewout W. Steyerberg
2. Andrew J. Vickers
3. Karel G. M. Moons
4. Eric R. Ziegel
5. Yuhai Wu
6. Douglas G. Altman
7. Gary S. Collins
8. Peter C. Austin
9. Johan A. K. Suykens
10. Anne-Laure Boulesteix

Who trusts B. Van calster overall

1. Evangelia Christodoulou
2. Tilmann Gneiting
3. R. Riley
4. Gary S. Collins
5. Jie Ma
6. Johannes Resin
7. Laure Wynants
8. Lieven Billiet
9. J. Verbakel
10. Ewout W. Steyerberg

Papers from B. Van calster's perspective

Ordered by how strongly the researchers B. Van calster trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
2. Regression Modeling Strategies (Technometrics)
3. Clinical Prediction Models
4. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests (British medical journal)
5. A calibration hierarchy for risk models was defined: from utopia to empirical data. (Journal of Clinical Epidemiology)
6. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. (Journal of Clinical Epidemiology)
7. Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. (The Lancet)
8. EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. (European Urology)
9. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
10. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints (BMC Medical Research Methodology)

Their papers

• A calibration hierarchy for risk models was defined: from utopia to empirical data. (2016) — 729 citations
• Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests (2016) — 1,011 citations
• Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence (2021) — 676 citations
• Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal (2020) — 2,435 citations
• A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019) — 1,645 citations
• A simple, step-by-step guide to interpreting decision curve analysis (2019) — 915 citations
• Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators (2018) — 1,022 citations
• A Mathematical Model for Interpretable Clinical Decision Support with Applications in Gynecology (2012) — 39 citations