Your browser doesn't support javascript.
loading
Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.
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; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address: paula.dhiman@csm.ox.a
  • Ma J; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.
  • Andaur Navarro CL; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Speich B; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; Meta-Research Centre, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland.
  • Bullock G; Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK.
  • Damen JAA; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Hooft L; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Kirtley S; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.
  • Riley RD; Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK, ST5 5BG.
  • Van Calster B; Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands; EPI-centre, KU Leuven, Leuven, Belgium.
  • Moons KGM; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
  • Collins GS; Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
J Clin Epidemiol ; 157: 120-133, 2023 05.
Article en En | MEDLINE | ID: mdl-36935090
OBJECTIVES: In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING: We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS: We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION: The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Investigación / Oncología Médica Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Investigación / Oncología Médica Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: J Clin Epidemiol Asunto de la revista: EPIDEMIOLOGIA Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos