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Richard D Riley

Showing results (211-220 of 293) with videos related to

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Statistics in Medicine|May 1, 2020
Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planningRichard D Riley, Thomas P A Debray, David Fisher, et al.
Annals of the Rheumatic Diseases|October 20, 2018
Development and validation of prediction models to estimate risk of primary total hip and knee replacements using data from the UK: two prospective open cohorts using the UK Clinical Practice Research DatalinkDahai Yu, Kelvin P Jordan, Kym I E Snell, et al.
Statistics in Medicine|May 9, 2024
Calibration plots for multistate risk predictions modelsAlexander Pate, Matthew Sperrin, Richard D Riley, et al.
Journal of Clinical Epidemiology|July 2, 2021
Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improvedPaula Dhiman, Jie Ma, Constanza Andaur Navarro, et al.
Journal of Clinical Epidemiology|December 17, 2022
Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic reviewMohammed T Hudda, Lucinda Archer, Maarten van Smeden, et al.
BMJ (Clinical Research Ed.)|May 3, 2023
Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)Kym I E Snell, Brooke Levis, Johanna A A Damen, et al.
BMJ Open|November 12, 2020
Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniquesConstanza L Andaur Navarro, Johanna A A G Damen, Toshihiko Takada, et al.
BMC Medical Research Methodology|January 14, 2022
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic reviewConstanza L Andaur Navarro, Johanna A A Damen, Toshihiko Takada, et al.
BMJ (Clinical Research Ed.)|October 21, 2021
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic reviewConstanza L Andaur Navarro, Johanna A A Damen, Toshihiko Takada, et al.
Journal of Clinical Epidemiology|April 6, 2023
Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction modelsConstanza L Andaur Navarro, Johanna A A Damen, Toshihiko Takada, et al.
Pageof 30

Showing results (211-220 of 293) with videos related to

Sort By:
Pageof 30
Statistics in Medicine|May 1, 2020
Individual participant data meta-analysis to examine interactions between treatment effect and participant-level covariates: Statistical recommendations for conduct and planningRichard D Riley, Thomas P A Debray, David Fisher, et al.
Annals of the Rheumatic Diseases|October 20, 2018
Development and validation of prediction models to estimate risk of primary total hip and knee replacements using data from the UK: two prospective open cohorts using the UK Clinical Practice Research DatalinkDahai Yu, Kelvin P Jordan, Kym I E Snell, et al.
Statistics in Medicine|May 9, 2024
Calibration plots for multistate risk predictions modelsAlexander Pate, Matthew Sperrin, Richard D Riley, et al.
Journal of Clinical Epidemiology|July 2, 2021
Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improvedPaula Dhiman, Jie Ma, Constanza Andaur Navarro, et al.
Journal of Clinical Epidemiology|December 17, 2022
Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic reviewMohammed T Hudda, Lucinda Archer, Maarten van Smeden, et al.
BMJ (Clinical Research Ed.)|May 3, 2023
Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA)Kym I E Snell, Brooke Levis, Johanna A A Damen, et al.
BMJ Open|November 12, 2020
Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniquesConstanza L Andaur Navarro, Johanna A A G Damen, Toshihiko Takada, et al.
BMC Medical Research Methodology|January 14, 2022
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic reviewConstanza L Andaur Navarro, Johanna A A Damen, Toshihiko Takada, et al.
BMJ (Clinical Research Ed.)|October 21, 2021
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic reviewConstanza L Andaur Navarro, Johanna A A Damen, Toshihiko Takada, et al.
Journal of Clinical Epidemiology|April 6, 2023
Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction modelsConstanza L Andaur Navarro, Johanna A A Damen, Toshihiko Takada, et al.
Pageof 30