Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review.
Dhiman P. et al, (2023), Bmc med res methodol, 23
Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review.
Ma J. et al, (2023), J clin epidemiol
Systematic review highlights high risk of bias of clinical prediction models for blood transfusion in patients undergoing elective surgery.
Dhiman P. et al, (2023), J clin epidemiol, 159, 10 - 30
Systematic review finds "Spin" practices and poor reporting standards in studies on machine learning-based prediction models.
Andaur Navarro CL. et al, (2023), J clin epidemiol
Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.
Andaur Navarro CL. et al, (2022), J clin epidemiol
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review.
Andaur Navarro CL. et al, (2022), Bmc med res methodol, 22
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.
Andaur Navarro CL. et al, (2021), Bmj, 375
Completeness of reporting of clinical prediction models developed using supervised machine learning: A systematic review
Andaur Navarro CL. et al, (2021)
There are no shortcuts in the development and validation of a COVID-19 prediction model.
Collins GS. et al, (2021), Transbound emerg dis, 68, 210 - 211
Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.
Andaur Navarro CL. et al, (2020), Bmj open, 10
Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal
Wynants L. et al, (2020), Bmj, m1328 - m1328
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models.
Christodoulou E. et al, (2019), J clin epidemiol, 110, 12 - 22
Risk prediction models in perioperative medicine: methodological considerations
Collins GS. et al, (2016), Current anesthesiology reports