Gary S. Collins

Statistics and Probability · 2014–2022

A researcher in the Science corpus: 31 papers; trusts 63 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 379 citations into it; #3,742 of 12,932 by trust here, #417 by citations
• Economics and Econometrics — 157 citations into it; #8,419 of 23,481 by trust here, #1,837 by citations
• Artificial Intelligence — 137 citations into it
• Cardiology and Cardiovascular Medicine — 134 citations into it; #395 of 906 by trust here, #232 by citations
• Epidemiology — 125 citations into it; #586 of 2,148 by trust here, #523 by citations
• General Health Professions — 108 citations into it; #2,697 of 4,781 by trust here, #623 by citations
• Radiology, Nuclear Medicine and Imaging — 96 citations into it
• Surgery — 94 citations into it

Whom Gary S. Collins trusts overall

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

1. Karel G. M. Moons
2. Douglas G. Altman
3. Patrick Royston
4. Y. Vergouwe
5. Ewout W. Steyerberg
6. A. Kengne
7. David Moher
8. for the Statistics Subcommittee of the NCI—EORTC Worki Diagnostics
9. E. von Elm
10. Diederick E. Grobbee

Who trusts Gary S. Collins overall

1. Tammy Jiang
2. Johannes B Reitsma
3. Karel G. M. Moons
4. Benjamin Alan Goldstein
5. R. Riley
6. Ewout W. Steyerberg
7. Xiaofeng Wang
8. Mark P. Sendak
9. J. Gradus
10. Douglas G. Altman

Papers from Gary S. Collins's perspective

Ordered by how strongly the researchers Gary S. Collins trusts endorse each paper. Same corpus, a different order for every researcher.

1. Regression Modeling Strategies (Technometrics)
2. Updating methods improved the performance of a clinical prediction model in new patients. (Journal of Clinical Epidemiology)
3. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies (British medical journal)
4. Multiple imputation using chained equations: Issues and guidance for practice (Statistics in Medicine)
5. Value of assessment of pretest probability of deep-vein thrombosis in clinical management.
6. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers (Statistics in Medicine)
7. The Revised CONSORT Statement for Reporting Randomized Trials: Explanation and Elaboration (Annals of Internal Medicine)
8. Risk prediction models: II. External validation, model updating, and impact assessment (Heart)
9. Evaluation of the Quality of Prognosis Studies in Systematic Reviews (Annals of Internal Medicine)
10. External validation of a Cox prognostic model: principles and methods (BMC Medical Research Methodology)

Their papers

• Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement (2015) — 894 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
• 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
• Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist (2014) — 1,708 citations
• Sample size considerations for the external validation of a multivariable prognostic model: a resampling study (2015) — 591 citations
• PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies (2019) — 2,019 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
• PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration (2019) — 1,227 citations
• Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement (2015) — 368 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
• Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. (2015) — 1,177 citations
• Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018) — 1,038 citations
• Handling time varying confounding in observational research (2017) — 333 citations
• Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015) — 899 citations
• Metrics Reloaded: Recommendations for image analysis validation (2022) — 479 citations
• Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990-2019: results from the Global Burden of Disease Study 2019 (2022) — 738 citations
• Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review (2021) — 338 citations