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Risk of bias of prognostic models developed using machine learning: a systematic review in oncology.
Dhiman, Paula; Ma, Jie; Andaur Navarro, Constanza L; Speich, Benjamin; Bullock, Garrett; Damen, Johanna A A; Hooft, Lotty; Kirtley, Shona; Riley, Richard D; Van Calster, Ben; Moons, Karel G M; Collins, Gary S.
Afiliación
  • Dhiman P; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK. paula.dhiman@ndorms.ox.ac.uk.
  • Ma J; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. paula.dhiman@ndorms.ox.ac.uk.
  • Andaur Navarro CL; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
  • Speich B; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Bullock G; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Damen JAA; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
  • Hooft L; Department of Clinical Research, Basel Institute for Clinical Epidemiology and Biostatistics, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Kirtley S; Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
  • Riley RD; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Van Calster B; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Moons KGM; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Collins GS; Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Diagn Progn Res ; 6(1): 13, 2022 Jul 07.
Article en En | MEDLINE | ID: mdl-35794668
BACKGROUND: Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain. METHODS: We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately. RESULTS: We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation. CONCLUSIONS: The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Diagn Progn Res Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Diagn Progn Res Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido