Riley, Richard D.

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

Systematic review and critical appraisal of prediction models for diagnosis and prognosis of COVID-19 infection

Wynants, L., Van Calster, B., Bonten, M. M., Collins, G. S., Debray, T. P., De Vos, M., Haller, M. C., Heinze, G., Moons, K. G., Riley, R. D., et al. (2020) · BMJ

COVID-19 Clinical Research StudiesStatistical Methods in Epidemiology

Cited by 3,070

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., Mallett, S., & for the PROBAST Group† (2019) · Annals of Internal Medicine

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 2,984

Calculating the sample size required for developing a clinical prediction model

Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020) · BMJ

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 2,917

Interpretation of random effects meta-analyses

Riley, R. D., Higgins, J. P. T., & Deeks, J. J. (2011) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 2,884

Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups

Dixon-Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A., Harvey, J., Hsu, R., Katbamna, S., Olsen, R., Smith, L., et al. (2006) · BMC Medical Research Methodology

Meta-analysis and systematic reviewsPublic Health and Social Inequalities

Cited by 2,289

Preferred Reporting Items for a Systematic Review and Meta-analysis of Individual Participant Data: The PRISMA-IPD Statement

Stewart, L. A., Clarke, M., Rovers, M., Riley, R. D., Simmonds, M., Stewart, G., & Tierney, J. F. (2015) · JAMA

Meta-analysis and systematic reviewsDelphi Technique in Research

Cited by 2,134

Meta-analysis of individual participant data: rationale, conduct, and reporting

Riley, R. D., Lambert, P. C., & Abo-Zaid, G. (2010) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,701

PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

Moons, K. G. M., Wolff, R. F., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. (2019) · Annals of Internal Medicine

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,669

Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research

Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., Altman, D. G., & for the PROGRESS Group (2013) · PLoS Medicine

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 1,508

Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes

Riley, R. D., Snell, K. I., Ensor, J., Burke, D. L., Harrell Jr, F. E., Moons, K. G., & Collins, G. S. (2019) · Statistics in Medicine

Statistical Methods in EpidemiologyStatistical Methods and Inference

Cited by 1,409

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

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

Collins, G. S., Dhiman, P., Andaur Navarro, C. L., Ma, J., Hooft, L., Reitsma, J. B., Logullo, P., Beam, A. L., Peng, L., Van Calster, B., et al. (2021) · BMJ Open

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 852

Prognosis Research Strategy (PROGRESS) 2: Prognostic Factor Research

Riley, R. D., Hayden, J. A., Steyerberg, E. W., Moons, K. G. M., Abrams, K., Kyzas, P. A., Malats, N., Briggs, A., Schroter, S., Altman, D. G., et al. (2013) · PLoS Medicine

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 823

Evaluation of clinical prediction models (part 1): from development to external validation

Collins, G. S., Dhiman, P., Ma, J., Schlussel, M. M., Archer, L., Van Calster, B., Harrell, F. E., Martin, G. P., Moons, K. G. M., van Smeden, M., et al. (2024) · BMJ

Statistical Methods in EpidemiologyArtificial Intelligence in Healthcare

Cited by 798

A guide to systematic review and meta-analysis of prediction model performance

Debray, T. P. A., Damen, J. A. A. G., Snell, K. I. E., Ensor, J., Hooft, L., Reitsma, J. B., Riley, R. D., & Moons, K. G. M. (2017) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 657

External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges

Riley, R. D., Ensor, J., Snell, K. I. E., Debray, T. P. A., Altman, D. G., Moons, K. G. M., & Collins, G. S. (2016) · BMJ

Statistical Methods in EpidemiologyMachine Learning in Healthcare

Cited by 585

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

Jackson, D., White, I. R., & Riley, R. D. (2012) · Statistics in Medicine

Meta-analysis and systematic reviewsStatistical Methods and Bayesian Inference

Cited by 577

Meta-analysis using individual participant data: One-stage and two-stage approaches, and why they may differ

Burke, D. L., Ensor, J., & Riley, R. D. (2017) · Statistics in Medicine

Meta-analysis and systematic reviewsStatistical Methods and Bayesian Inference

Cited by 538

Multivariate meta‐analysis: Potential and promise

Jackson, D., Riley, R., & White, I. R. (2011) · Statistics in Medicine

Meta-analysis and systematic reviewsStatistical Methods and Bayesian Inference

Cited by 530

GRADE guidelines 28: use of grade for the assessment of evidence about prognostic factors: rating certainty in identification of groups of patients with different absolute risks

Foroutan, F., Guyatt, G., Zuk, V., Vandvik, P. O., Alba, A. C., Mustafa, R., Vernooij, R., Arevalo-Rodriguez, I., Munn, Z., Roshanov, P., et al. (2020) · Journal of Clinical Epidemiology

Meta-analysis and systematic reviewsClinical practice guidelines implementation

Cited by 465

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

Andaur Navarro, C. L., Damen, J. A. A., Takada, T., Nijman, S. W. J., Dhiman, P., Ma, J., Collins, G. S., Bajpai, R., Riley, R. D., Moons, K. G. M., et al. (2021) · BMJ

Statistical Methods in EpidemiologyMeta-analysis and systematic reviews

Cited by 415

Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey

Ahmed, I., Sutton, A. J., & Riley, R. D. (2012) · BMJ

Meta-analysis and systematic reviewsPharmaceutical industry and healthcare

Cited by 404

Minimum sample size for external validation of a clinical prediction model with a binary outcome

Riley, R. D., Debray, T. P. A., Collins, G. S., Archer, L., Ensor, J., van Smeden, M., & Snell, K. I. E. (2021) · Statistics in Medicine

Statistical Methods in EpidemiologyStatistical Methods in Clinical Trials

Cited by 400