|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
|Interpretation of random effects meta-analyses|
RD Riley, JPT Higgins, JJ Deeks
Bmj 342, 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
|Meta-analysis of individual participant data: rationale, conduct, and reporting|
RD Riley, PC Lambert, G Abo-Zaid
Bmj 340, 2010
|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
|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
|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
|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
|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
|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
|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
|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
|Prognosis research strategy (PROGRESS) 4: stratified medicine research|
AD Hingorani, DA van der Windt, RD Riley, K Abrams, KGM Moons, ...
Bmj 346, 2013
|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
|Multivariate meta‐analysis: potential and promise|
D Jackson, R Riley, IR White
Statistics in medicine 30 (20), 2481-2498, 2011
|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
|Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey|
I Ahmed, AJ Sutton, RD Riley
Bmj 344, 2012
|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
|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
|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