R. Riley

Statistics and Probability · British medical journal · 2009–2021

A researcher in the Science corpus: 18 papers; trusts 47 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 225 citations into it; #1,363 of 12,932 by trust here, #475 by citations
• Economics and Econometrics — 79 citations into it; #5,295 of 23,481 by trust here, #6,570 by citations
• Artificial Intelligence — 63 citations into it
• Epidemiology — 55 citations into it; #758 of 2,148 by trust here, #687 by citations
• Radiology, Nuclear Medicine and Imaging — 53 citations into it
• General Health Professions — 43 citations into it
• Public Health, Environmental and Occupational Health — 36 citations into it
• Cardiology and Cardiovascular Medicine — 33 citations into it; #461 of 906 by trust here, #256 by citations

Whom R. Riley trusts overall

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

1. Douglas G. Altman
2. Ewout W. Steyerberg
3. Karel G. M. Moons
4. Julian P. T. Higgins
5. H. V. van Houwelingen
6. Johannes B Reitsma
7. Patrick Royston
8. R. Harbord
9. Haitao Chu
10. Allan Donner

Who trusts R. Riley overall

1. Ian R. White
2. Dan Jackson
3. Mike W.-L. Cheung
4. Karel G. M. Moons
5. G. Stead
6. J. Ensor
7. D. Burke
8. Julian P. T. Higgins
9. Linyu Shi
10. Gary S. Collins

Papers from R. Riley's perspective

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

1. Updating methods improved the performance of a clinical prediction model in new patients. (Journal of Clinical Epidemiology)
2. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS
3. Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research (PLoS Medicine)
4. Measuring inconsistency in meta-analyses (British medical journal)
5. Prediction of Postoperative Pulmonary Complications in a Population-based Surgical Cohort (Anesthesiology)
6. Dichotomizing continuous predictors in multiple regression: a bad idea (Statistics in Medicine)
7. WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility (Statistics and computing)
8. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers (Statistics in Medicine)
9. Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. (American Journal of Epidemiology)
10. Value of assessment of pretest probability of deep-vein thrombosis in clinical management.

Their papers

• Interpretation of random effects meta-analyses (2011) — 2,529 citations
• Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement. (2015) — 1,945 citations
• Multivariate meta-analysis: Potential and promise (2011) — 517 citations
• Rejoinder to commentaries on ‘Multivariate meta‐analysis: Potential and promise’ (2011) — 166 citations
• Meta-analysis of individual participant data: rationale, conduct, and reporting (2010) — 1,698 citations
• Multivariate meta‐analysis: the effect of ignoring within‐study correlation (2009) — 213 citations
• Calculating the sample size required for developing a clinical prediction model (2020) — 2,194 citations
• Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research (2013) — 748 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
• PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration (2019) — 1,227 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
• Minimum sample size for developing a multivariable prediction model: PART II ‐ binary and time‐to‐event outcomes (2018) — 1,038 citations
• Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review (2021) — 338 citations
• Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal (2020) — 2,435 citations
• A guide to systematic review and meta-analysis of prognostic factor studies (2019) — 656 citations
• Meta‐analysis using individual participant data: one‐stage and two‐stage approaches, and why they may differ (2016) — 468 citations
• Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research (2013) — 1,371 citations