| Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal L Wynants, B Van Calster, GS Collins, RD Riley, G Heinze, E Schuit, ... bmj 369, 2020 | 3262 | 2020 |
| Interpretation of random effects meta-analyses RD Riley, JPT Higgins, JJ Deeks Bmj 342, 2011 | 2485 | 2011 |
| Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups M Dixon-Woods, D Cavers, S Agarwal, E Annandale, A Arthur, J Harvey, ... BMC medical research methodology 6, 1-13, 2006 | 2289 | 2006 |
| Preferred reporting items for a systematic review and meta-analysis of individual participant data: the PRISMA-IPD statement LA Stewart, M Clarke, M Rovers, RD Riley, M Simmonds, G Stewart, ... Jama 313 (16), 1657-1665, 2015 | 1918 | 2015 |
| Meta-analysis of individual participant data: rationale, conduct, and reporting RD Riley, PC Lambert, G Abo-Zaid Bmj 340, 2010 | 1857 | 2010 |
| PROBAST: a tool to assess the risk of bias and applicability of prediction model studies RF Wolff, KGM Moons, RD Riley, PF Whiting, M Westwood, GS Collins, ... Annals of internal medicine 170 (1), 51-58, 2019 | 1607 | 2019 |
| Calculating the sample size required for developing a clinical prediction model RD Riley, J Ensor, KIE Snell, FE Harrell, GP Martin, JB Reitsma, ... Bmj 368, 2020 | 1530 | 2020 |
| Prognosis Research Strategy (PROGRESS) 3: prognostic model research EW Steyerberg, KGM Moons, DA van der Windt, JA Hayden, P Perel, ... PLoS medicine 10 (2), e1001381, 2013 | 1511 | 2013 |
| PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration KGM Moons, RF Wolff, RD Riley, PF Whiting, M Westwood, GS Collins, ... Annals of internal medicine 170 (1), W1-W33, 2019 | 1031 | 2019 |
| Prognosis Research Strategy (PROGRESS) 2: prognostic factor research RD Riley, JA Hayden, EW Steyerberg, KGM Moons, K Abrams, PA Kyzas, ... PLoS medicine 10 (2), e1001380, 2013 | 854 | 2013 |
| Minimum sample size for developing a multivariable prediction model: PART II‐binary and time‐to‐event outcomes RD Riley, KIE Snell, J Ensor, DL Burke, FE Harrell Jr, KGM Moons, ... Statistics in medicine 38 (7), 1276-1296, 2019 | 834 | 2019 |
| Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes H Hemingway, P Croft, P Perel, JA Hayden, K Abrams, A Timmis, A Briggs, ... Bmj 346, 2013 | 698 | 2013 |
| Prognosis research strategy (PROGRESS) 4: stratified medicine research AD Hingorani, DA van der Windt, RD Riley, K Abrams, KGM Moons, ... Bmj 346, 2013 | 589 | 2013 |
| A guide to systematic review and meta-analysis of prognostic factor studies RD Riley, KGM Moons, KIE Snell, J Ensor, L Hooft, DG Altman, J Hayden, ... bmj 364, 2019 | 565 | 2019 |
| Multivariate meta‐analysis: potential and promise D Jackson, R Riley, IR White Statistics in medicine 30 (20), 2481-2498, 2011 | 563 | 2011 |
| Quantifying the impact of between‐study heterogeneity in multivariate meta‐analyses D Jackson, IR White, RD Riley Statistics in medicine 31 (29), 3805-3820, 2012 | 560 | 2012 |
| 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 … GS Collins, P Dhiman, CLA Navarro, J Ma, L Hooft, JB Reitsma, P Logullo, ... BMJ open 11 (7), e048008, 2021 | 522 | 2021 |
| External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges RD Riley, J Ensor, KIE Snell, TPA Debray, DG Altman, KGM Moons, ... bmj 353, 2016 | 506 | 2016 |
| A guide to systematic review and meta-analysis of prediction model performance TPA Debray, JAAG Damen, KIE Snell, J Ensor, L Hooft, JB Reitsma, ... bmj 356, 2017 | 483 | 2017 |
| Meta‐analysis using individual participant data: one‐stage and two‐stage approaches, and why they may differ DL Burke, J Ensor, RD Riley Statistics in medicine 36 (5), 855-875, 2017 | 480 | 2017 |