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Nature Clinical Practice. Oncology|November 3, 2005
Primer: an evidence-based approach to prognostic markersDouglas G Altman, Richard D RileyStatistics in Medicine|June 17, 2017
Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practiceBrian H Willis, Richard D RileyResearch Synthesis Methods|June 10, 2015
Meta-analysis of a binary outcome using individual participant data and aggregate dataRichard D Riley, Ewout W SteyerbergJournal of Clinical Epidemiology|April 17, 2024
SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction modelsConstanza L Andaur Navarro, Johanna A A Damen, Mona Ghannad, et al.Expert Review of Pharmacoeconomics & Outcomes Research|October 8, 2009
Quality of life in breast cancer patients: measurement issues in cancer clinical trialsFabio Efficace, Andrew Bottomley, Gary S CollinsJournal 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.Statistics in Medicine|November 4, 2014
An alternative pseudolikelihood method for multivariate random-effects meta-analysisYong Chen, Chuan Hong, Richard D RileyResearch Synthesis Methods|October 22, 2017
An improved method for bivariate meta-analysis when within-study correlations are unknownChuan Hong, Richard D Riley, Yong ChenBreast Cancer Research and Treatment|November 1, 2011
A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performanceCatherine Meads, Ikhlaaq Ahmed, Richard D RileyPageof 51