Constanza L. Andaur Navarro

Risk of bias of prognostic models developed using machine learning: a systematic review in oncology

G. Collins, B. van Calster, L. Hooft, 2022, Diagnostic and Prognostic Research.

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.

G. Collins, L. Hooft, R. Bajpai, 2022, Journal of clinical epidemiology.

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review

G. Collins, B. van Calster, L. Hooft, 2022, BMC Medical Research Methodology.

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

G. Collins, L. Hooft, R. Bajpai, 2021, BMJ.

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

L. Peng, G. Collins, B. van Calster, 2021, BMJ Open.

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review

G. Collins, L. Hooft, R. Bajpai, 2021, BMC Medical Research Methodology.

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques

G. Collins, L. Hooft, R. Bajpai, 2020, BMJ Open.

Systematic review finds "Spin" practices and poor reporting standards in studies on machine learning-based prediction models.

L. Hooft, R. Riley, K. Moons, 2023, Journal of clinical epidemiology.