Stability of clinical prediction models developed using statistical or machine learning methods by Riley, RD, Collins, GS

COVID-19 prediction models should adhere to methodological and reporting standards by Collins, GS, van Smeden, M, Riley, RD

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 by Riley, RD, Van Calster, B, Collins, GS

Flaws in the development and validation of a covid-19 prediction model by Collins, GS, Riley, RD, van Smeden, M

Developing clinical prediction models when adhering to minimum sample size recommendations: the importance of quantifying bootstrap variability in tuning parameters and predictive... by Martin, GP, Riley, RD, Collins, GS, Sperrin, M

Targeted validation: validating clinical prediction models in their intended population and setting by Sperrin, M, Riley, RD, Collins, GS, Martin, GP

Calculating the power of a planned individual participant data meta-analysis of randomised trials to examine a treatment-covariate interaction with a time-to-event outcome by Riley, RD, Collins, GS, Hattle, M, Whittle, R, Ensor, J

Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome by Riley, RD, Hattle, M, Collins, GS, Whittle, R, Ensor, J

Calculating the power of a planned individual participant data meta‐analysis to examine prognostic factor effects for a binary outcome by Whittle, R, Ensor, J, Hattle, M, Dhiman, P, Collins, GS, Riley, RD

Clinical prediction models and the multiverse of madness by Riley, RD, Pate, A, Dhiman, P, Archer, L, Martin, GP, Collins, GS

Methodology over metrics: current scientific standards are a disservice to patients and society by Van Calster, B, Wynants, L, Riley, RD, van Smeden, M, Collins, GS

Minimum sample size for external validation of a clinical prediction model with a continuous outcome by Archer, L, Snell, KIE, Ensor, J, Hudda, MT, Collins, GS, Riley, RD

Doug Altman: Driving critical appraisal and improvements in the quality of methodological and medical research by Sauerbrei, W, Bland, M, Evans, SJW, Riley, RD, Royston, P, Schumacher, M, Collins, GS

Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems? by Jenkins, DA, Martin, GP, Sperrin, M, Riley, RD, Debray, TPA, Collins, GS, Peek, N

Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small by Riley, RD, Snell, KIE, Martin, GP, Whittle, R, Archer, L, Sperrin, M, Collins, GS

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review by Dhiman, P, Ma, J, Qi, C, Bullock, G, Sergeant, JC, Riley, RD, Collins, GS

Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review by Ma, J, Dhiman, P, Qi, C, Bullock, G, van Smeden, M, Riley, RD, Collins, GS

Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist by Debray, TPA, Collins, GS, Riley, RD, Snell, KIE, Van Calster, B, Reitsma, JB, Moons, KGM

Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration by Debray, TPA, Collins, GS, Riley, RD, Snell, KIE, Van Calster, B, Reitsma, JB, Moons, KGM

Minimum sample size for external validation of a clinical prediction model with a binary outcome by Riley, RD, Debray, TPA, Collins, GS, Archer, L, Ensor, J, van Smeden, M, Snell, KIE