Dhiman, P, Ma, J, Navarro, CA, Speich, B, Bullock, G, Damen, JA, Kirtley, S, Hooft, L, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Van Calster, B, Moons, KGM and Collins, GS (2021) Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved. Journal of Clinical Epidemiology, 138. pp. 60-72.

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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.

Item Type: Article
Additional Information: The final version of this publication is available directly from the publishers at https://www.jclinepi.com/article/S0895-4356(21)00202-X/fulltext#seccesectitle0001 © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license
Uncontrolled Keywords: prediction; machine learning; reporting
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > School of Medicine
Related URLs:
Depositing User: Symplectic
Date Deposited: 06 Jul 2021 15:04
Last Modified: 21 Oct 2021 14:11
URI: https://eprints.keele.ac.uk/id/eprint/9804

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