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A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
PLoS ONE 16:e0251194 (2021)
Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of longitudinal data of 11 articles published in PLOS ONE. Inclusion criteria were the availability of data and author consent. We investigated the types of methods and software used and whether we were able to reproduce the data analysis using open source software. Most articles provided overview tables and simple visualisations. Generalised Estimating Equations (GEEs) were the most popular statistical models among the selected articles. Only one article used open source software and only one published part of the analysis code. Replication was difficult in most cases and required reverse engineering of results or contacting the authors. For three articles we were not able to reproduce the results, for another two only parts of them. For all but two articles we had to contact the authors to be able to reproduce the results. Our main learning is that reproducing papers is difficult if no code is supplied and leads to a high burden for those conducting the reproductions. Open data policies in journals are good, but to truly boost reproducibility we suggest adding open code policies.
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Publication type
Article: Journal article
Document type
Scientific Article
Language
english
Publication Year
2021
HGF-reported in Year
2021
ISSN (print) / ISBN
1932-6203
Journal
PLoS ONE
Quellenangaben
Volume: 16,
Issue: 6,
Article Number: e0251194
Publisher
Public Library of Science (PLoS)
Publishing Place
Lawrence, Kan.
Reviewing status
Peer reviewed
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-530004-001
Grants
German Federal Ministry of Education and Research (BMBF)
WOS ID
WOS:000671691000011
Scopus ID
85108352539
PubMed ID
34153038
Erfassungsdatum
2021-07-13