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

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Dhiman Paula, Ma Jie, Navarro Constanza Andaur, Speich Benjamin, Bullock Garrett, Damen Johanna AA, Kirtley Shona, Hooft Lotty, Riley Richard D, Van Calster Ben, Moons Karel G.M., Collins Gary S.,
Project Improving the reporting in randomised clinical trials: How reliable are clinical trial registries and how efficient is the use of reporting checklists for peer reviewers?
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Original article (peer-reviewed)

Journal Journal of Clinical Epidemiology
Volume (Issue) 138
Page(s) 60 - 72
Title of proceedings Journal of Clinical Epidemiology
DOI 10.1016/j.jclinepi.2021.06.024

Open Access

URL http://doi.org/10.1016/j.jclinepi.2021.06.024
Type of Open Access Publisher (Gold Open Access)

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.
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