Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review-Reference-Cited by-同舟云学术

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Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review

Published:2022-02 Issue: Volume:142 Page:218-229
ISSN:0895-4356
Container-title:Journal of Clinical Epidemiology
language:en
Short-container-title:Journal of Clinical Epidemiology

Author:

Nijman SWJ^ORCID,Leeuwenberg AM^ORCID,Beekers I,Verkouter I,Jacobs JJL,Bots ML,Asselbergs FW,Moons KGM,Debray TPA^ORCID

Publisher

Elsevier BV

Subject

Epidemiology

Reference42 articles.

1. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration;Vandenbroucke;PLoS Med,2007

2. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement;Collins;BMC Med,2015

3. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls;Sterne;BMJ,2009

4. Randomized trials with missing outcome data: how to analyze and what to report;Groenwold;CMAJ,2014

5. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration;Moons;Ann Intern Med,2015

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