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Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved.

Abstract

OBJECTIVE: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology. STUDY DESIGN AND SETTING: We conducted a systematic review, searching the MEDLINE and Embase databases between 01/01/2019 and 05/09/2019, for non-imaging studies developing a prognostic clinical prediction model using machine learning methods (as defined by primary study authors) in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement to assess the reporting quality of included publications. We described overall reporting adherence of included publications and by each section of TRIPOD. RESULTS: Sixty-two publications met the inclusion criteria. 48 were development studies and 14 were development with validation studies. 152 models were developed across all publications. Median adherence to TRIPOD reporting items was 41% [range: 10%-67%] and at least 50% adherence was found in 19% (n=12/62) of publications. Adherence was lower in development only studies (median: 38% [range: 10%-67%]); and higher in development with validation studies (median: 49% [range: 33%-59%]). CONCLUSION: Reporting of clinical prediction models using machine learning in oncology is poor and needs urgent improvement, so readers and stakeholders can appraise the study methods, understand study findings, and reduce research waste.

Citation

(2021). Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. Journal of Clinical Epidemiology, 60-72. https://doi.org/10.1016/j.jclinepi.2021.06.024

Acceptance Date Jun 25, 2021
Publication Date Oct 1, 2021
Journal Journal of Clinical Epidemiology
Print ISSN 0895-4356
Publisher Elsevier
Pages 60-72
DOI https://doi.org/10.1016/j.jclinepi.2021.06.024
Keywords prediction; machine learning; reporting
Publisher URL https://www.jclinepi.com/article/S0895-4356(21)00202-X/fulltext#seccesectitle0001

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