[up] Up a level
Group by: Item Type | No Grouping
Jump to: Article
Number of items: 38.

Article

Snell, KIE, Levis, B, Damen, JAA, Dhiman, P, Debray, TPA, Hooft, L, Reitsma, JB, Moons, KGM, Collins, GS and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2023) Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 381. e073538 - ?.

Andaur Navarro, CL, Damen, JA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KG and Hooft, L (2023) Systematic review finds "Spin" practices and poor reporting standards in studies on machine learning-based prediction models. Journal of Clinical Epidemiology.

Debray, TPA, Collins, GS, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Van Calster, B, Reitsma, JB and Moons, KGM (2023) Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ, 380. e071058 - ?.

Debray, TPA, Collins, GS, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Van Calster, B, Reitsma, JB and Moons, KGM (2023) Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ, 380.

Pate, A, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Collins, GS, van Smeden, M, Van Calster, B, Ensor, J and Martin, GP (2023) Minimum sample size for developing a multivariable prediction model using multinomial logistic regression. Statistical Methods in Medical Research, 32 (3). pp. 555-571.

Sanchez-Santos, MT, Williamson, E, Nicolson, PJA, Bruce, J, Collins, GS, Mallen, CD ORCID: https://orcid.org/0000-0002-2677-1028, Griffiths, F, Garret, A, Morris, A, Slark, M, Lamb, SE and OPAL study team, . (2022) Development and validation of a prediction model for self-reported mobility decline in community-dwelling older adults. Journal of Clinical Epidemiology, 152. pp. 70-79.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Hattle, M, Collins, GS, Whittle, R ORCID: https://orcid.org/0000-0003-1793-0135 and Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282 (2022) Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome. Statistics in Medicine.

Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2022) Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagnostic and Prognostic Research, 6 (1). 13 - ?.

Bullock, GS, Mylott, J, Hughes, T, Nicholson, KF, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Collins, GS (2022) Just How Confident Can We Be in Predicting Sports Injuries? A Systematic Review of the Methodological Conduct and Performance of Existing Musculoskeletal Injury Prediction Models in Sport. Sports Medicine.

Dhiman, P, Ma, J, Andaur Navarro, CL, Speich, B, Bullock, G, Damen, JAA, Hooft, L, Kirtley, S, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2022) Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Medical Research Methodology, 22 (1). 101 - ?.

Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R ORCID: https://orcid.org/0000-0002-1227-2703, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM and Hooft, L (2022) Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review. BMC Medical Research Methodology, 22 (1). 12 - ?.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Collins, GS, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Archer, L ORCID: https://orcid.org/0000-0003-2504-2613, Booth, S, Mozumder, SI, Rutherford, MJ, van Smeden, M, Lambert, PC and Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591 (2021) Minimum sample size calculations for external validation of a clinical prediction model with a time-to-event outcome. Statistics in Medicine.

Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM and Hooft, L (2021) Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ, 375. n2281 - ?.

Martin, GP, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Collins, GS and Sperrin, M (2021) Developing clinical prediction models when adhering to minimum sample size recommendations: The importance of quantifying bootstrap variability in tuning parameters and predictive performance. Statistical Methods in Medical Research, 30 (12). pp. 2545-2561.

Bullock, GS, Hughes, T, Sergeant, JC, Callaghan, MJ, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Collins, GS (2021) Clinical Prediction Models in Sports Medicine: A Guide for Clinicians and Researchers. Journal of Orthopaedic and Sports Physical Therapy, 51 (10). 517 - 525.

Dhiman, P, Ma, J, Navarro, CA, Speich, B, Bullock, G, Damen, JA, Kirtley, S, Hooft, L, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2021) Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. Journal of Clinical Epidemiology, 138. pp. 60-72.

Collins, GS, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and van Smeden, M (2021) Flaws in the development and validation of a covid-19 prediction model. Clinical Infectious Diseases, 73 (3). pp. 557-558.

Collins, GS, Dhiman, P, Andaur Navarro, CL, Ma, J, Hooft, L, Reitsma, JB, Logullo, P, Beam, AL, Peng, L, Van Calster, B, van Smeden, M, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 and Moons, KG (2021) 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. BMJ Open, 11 (7). e048008 - ?.

Andaur Navarro, CL, Damen, JAA, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R ORCID: https://orcid.org/0000-0002-1227-2703, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM and Hooft, L (2021) Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review. arXiv.org. (Unpublished)

Van Calster, B, Wynants, L, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, van Smeden, M and Collins, GS (2021) Methodology over metrics: Current scientific standards are a disservice to patients and society. Journal of Clinical Epidemiology, 138. pp. 219-226.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Debray, TPA, Collins, GS, Archer, L ORCID: https://orcid.org/0000-0003-2504-2613, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, van Smeden, M and Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591 (2021) Minimum sample size for external validation of a clinical prediction model with a binary outcome. Statistics in Medicine.

van Smeden, M, Reitsma, JB, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Collins, GS and Moons, KG (2021) Clinical prediction models: diagnosis versus prognosis. Journal of Clinical Epidemiology, 132. 142 - 145.

Navarro, CLA, Damen, J, Takada, T, Dhiman, P, Collins, GS, Ma, J, Bajpai, R ORCID: https://orcid.org/0000-0002-1227-2703, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Hooft, L and Moons, KG (2021) Why are Machine Learning-based prediction models still unpopular in clinical practice? Diagnostic and Prognostic Research, 5 (S2). 37 - 37.

Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Archer, L ORCID: https://orcid.org/0000-0003-2504-2613, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Bonnett, LJ, Debray, TP, Phillips, B, Collins, GS and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2021) External validation of clinical prediction models: simulation-based sample size calculations were more reliable than rules-of-thumb. Journal of Clinical Epidemiology.

Jenkins, DA, Martin, GP, Sperrin, M, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Debray, TPA, Collins, GS and Peek, N (2021) Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems? Diagnostic and Prognostic Research, 5 (1). 1 - ?.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B and Collins, GS (2020) A note on estimating the Cox-Snell R2 from a reported C statistic (AUROC) to inform sample size calculations for developing a prediction model with a binary outcome. Statistics in Medicine.

Andaur Navarro, CL, Damen, JAAG, Takada, T, Nijman, SWJ, Dhiman, P, Ma, J, Collins, GS, Bajpai, R ORCID: https://orcid.org/0000-0002-1227-2703, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KG and Hooft, L (2020) Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques. BMJ Open, 10 (11). e038832 - ?.

Archer, L, Snell, KIE, Ensor, J, Hudda, MT, Collins, GS and Riley, RD (2020) Minimum sample size for external validation of a clinical prediction model with a continuous outcome. Statistics in Medicine.

Collins, GS, van Smeden, M and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2020) COVID-19 prediction models should adhere to methodological and reporting standards. European Respiratory Journal.

Sauerbrei, W, Bland, M, Evans, SJW, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Royston, P, Schumacher, M and Collins, GS (2020) Doug Altman: Driving critical appraisal and improvements in the quality of methodological and medical research. Biometrical Journal.

Wynants, L, Van Calster, B, Bonten, MMJ, Collins, GS, Debray, TPA, De Vos, M, Haller, MC, Heinze, G, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Schuit, E, Smits, LJM, Snell, KIE ORCID: https://orcid.org/0000-0001-9373-6591, Steyerberg, EW, Wallisch, C and van Smeden, M (2020) Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal. BMJ, 369.

Bonnett, LJ, Snell, KIE, Collins, GS and Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735 (2019) Guide to presenting clinical prediction models for use in clinical settings. BMJ, 365.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KI, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Burke, DL ORCID: https://orcid.org/0000-0003-2803-1151, Harrell, FE, Moons, KG and Collins, GS (2019) Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Statistics in Medicine, 38 (7). pp. 1276-1296.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KI, Ensor, J, Burke, DL, Harrell, FE, Moons, KGM and Collins, GS (2019) Minimum sample size for developing a multivariable prediction model: Part II-binary and time-to-event outcomes. Statistics in Medicine, 38 (7). pp. 1276-1296.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Snell, KIE, Ensor, J, Burke, DL, Harrell, FE, Moons, KGM and Collins, GS (2019) Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes. Statistics in Medicine, 38 (7). 1262 - 1275.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Moons, KGM, Snell, KIE, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Hooft, L, Altman, DG, Hayden, J, Collins, GS and Debray, TPA (2019) A guide to systematic review and meta-analysis of prognostic factor studies. BMJ, 364.

Wolff, RF, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Whiting, PF, Westwood, M, Collins, GS, Reitsma, JB, Kleijnen, J, Mallett, S and PROBAST Group†, . (2019) PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of Internal Medicine, 170 (1). 51 - 58.

Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Ensor, J ORCID: https://orcid.org/0000-0001-7481-0282, Snell, KIE, Debray, TPA, Altman, DG, Moons, KGM and Collins, GS (2016) External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ, 353.

This list was generated on Tue Oct 31 07:54:23 2023 UTC.