Karel G. M. Moons

Statistics and Probability · 2003–2025

A researcher in the Science corpus: 52 papers; trusts 70 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 463 citations into it; #2,005 of 12,932 by trust here, #190 by citations
• Artificial Intelligence — 164 citations into it; #14,107 of 35,345 by trust here, #6,614 by citations
• Economics and Econometrics — 158 citations into it; #5,336 of 23,481 by trust here, #1,100 by citations
• Cardiology and Cardiovascular Medicine — 127 citations into it; #26 of 906 by trust here, #63 by citations
• Surgery — 117 citations into it; #809 of 1,106 by trust here, #372 by citations
• Public Health, Environmental and Occupational Health — 98 citations into it; #337 of 2,368 by trust here, #137 by citations
• Radiology, Nuclear Medicine and Imaging — 89 citations into it; #1,282 of 3,265 by trust here, #648 by citations
• Epidemiology — 88 citations into it; #69 of 2,148 by trust here, #172 by citations

Whom Karel G. M. Moons trusts overall

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

1. Douglas G. Altman
2. Patrick Royston
3. Ewout W. Steyerberg
4. Y. Vergouwe
5. Michael J. Pencina
6. Esteban Walker
7. J. Hayden
8. Nancy R. Cook
9. Pablo Perel
10. R. Riley

Who trusts Karel G. M. Moons overall

1. Gary S. Collins
2. Johannes B Reitsma
3. Douglas G. Altman
4. P. Mohapatra
5. Patrick Royston
6. R. Riley
7. S. van Buuren
8. Ewout W. Steyerberg
9. T. Debray
10. Y. Vergouwe

Papers from Karel G. M. Moons's perspective

Ordered by how strongly the researchers Karel G. M. Moons trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Modeling Strategies (Technometrics)
2. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis
3. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. (American Journal of Epidemiology)
4. Value of assessment of pretest probability of deep-vein thrombosis in clinical management.
5. Updating methods improved the performance of a clinical prediction model in new patients. (Journal of Clinical Epidemiology)
6. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies (British medical journal)
7. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers (Statistics in Medicine)
8. Multiple imputation using chained equations: Issues and guidance for practice (Statistics in Medicine)
9. Dichotomizing continuous predictors in multiple regression: a bad idea (Statistics in Medicine)
10. The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Their papers

• A new framework to enhance the interpretation of external validation studies of clinical prediction models. (2015) — 506 citations
• Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement (2015) — 894 citations
• Prognosis and prognostic research: Developing a prognostic model (2009) — 1,177 citations
• External validation of multivariable prediction models: a systematic review of methodological conduct and reporting (2014) — 646 citations
• Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration (2015) — 4,311 citations
• Prognosis and prognostic research: what, why, and how? (2009) — 1,324 citations
• Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement (2015) — 2,276 citations
• Calculating the sample size required for developing a clinical prediction model (2020) — 2,194 citations
• Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker (2012) — 850 citations
• Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research (2013) — 748 citations
• Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist (2014) — 1,708 citations
• Development and validation of MIX: comprehensive free software for meta-analysis of causal research data (2006) — 532 citations
• Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes (2013) — 596 citations
• Dealing with missing outcome data in randomized trials and observational studies. (2012) — 391 citations
• External validation is necessary in prediction research: a clinical example. (2003) — 709 citations
• PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies (2019) — 2,019 citations
• Get real in individual participant data (IPD) meta‐analysis: a review of the methodology (2015) — 307 citations
• External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016) — 510 citations
• Sample size for binary logistic prediction models: Beyond events per variable criteria (2018) — 679 citations
• Risk prediction models: II. External validation, model updating, and impact assessment (2012) — 1,118 citations