A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.

A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.

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 long...

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Autores principales: Heidi Seibold, Severin Czerny, Siona Decke, Roman Dieterle, Thomas Eder, Steffen Fohr, Nico Hahn, Rabea Hartmann, Christoph Heindl, Philipp Kopper, Dario Lepke, Verena Loidl, Maximilian Mandl, Sarah Musiol, Jessica Peter, Alexander Piehler, Elio Rojas, Stefanie Schmid, Hannah Schmidt, Melissa Schmoll, Lennart Schneider, Xiao-Yin To, Viet Tran, Antje Völker, Moritz Wagner, Joshua Wagner, Maria Waize, Hannah Wecker, Rui Yang, Simone Zellner, Malte Nalenz
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/cc4ab404fc9743df8c8df776f92f6ace
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spelling oai:doaj.org-article:cc4ab404fc9743df8c8df776f92f6ace2021-12-02T20:03:51ZA computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.1932-620310.1371/journal.pone.0251194https://doaj.org/article/cc4ab404fc9743df8c8df776f92f6ace2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251194https://doaj.org/toc/1932-6203Computational 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.Heidi SeiboldSeverin CzernySiona DeckeRoman DieterleThomas EderSteffen FohrNico HahnRabea HartmannChristoph HeindlPhilipp KopperDario LepkeVerena LoidlMaximilian MandlSarah MusiolJessica PeterAlexander PiehlerElio RojasStefanie SchmidHannah SchmidtMelissa SchmollLennart SchneiderXiao-Yin ToViet TranAntje VölkerMoritz WagnerJoshua WagnerMaria WaizeHannah WeckerRui YangSimone ZellnerMalte NalenzPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0251194 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Heidi Seibold
Severin Czerny
Siona Decke
Roman Dieterle
Thomas Eder
Steffen Fohr
Nico Hahn
Rabea Hartmann
Christoph Heindl
Philipp Kopper
Dario Lepke
Verena Loidl
Maximilian Mandl
Sarah Musiol
Jessica Peter
Alexander Piehler
Elio Rojas
Stefanie Schmid
Hannah Schmidt
Melissa Schmoll
Lennart Schneider
Xiao-Yin To
Viet Tran
Antje Völker
Moritz Wagner
Joshua Wagner
Maria Waize
Hannah Wecker
Rui Yang
Simone Zellner
Malte Nalenz
A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
description 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.
format article
author Heidi Seibold
Severin Czerny
Siona Decke
Roman Dieterle
Thomas Eder
Steffen Fohr
Nico Hahn
Rabea Hartmann
Christoph Heindl
Philipp Kopper
Dario Lepke
Verena Loidl
Maximilian Mandl
Sarah Musiol
Jessica Peter
Alexander Piehler
Elio Rojas
Stefanie Schmid
Hannah Schmidt
Melissa Schmoll
Lennart Schneider
Xiao-Yin To
Viet Tran
Antje Völker
Moritz Wagner
Joshua Wagner
Maria Waize
Hannah Wecker
Rui Yang
Simone Zellner
Malte Nalenz
author_facet Heidi Seibold
Severin Czerny
Siona Decke
Roman Dieterle
Thomas Eder
Steffen Fohr
Nico Hahn
Rabea Hartmann
Christoph Heindl
Philipp Kopper
Dario Lepke
Verena Loidl
Maximilian Mandl
Sarah Musiol
Jessica Peter
Alexander Piehler
Elio Rojas
Stefanie Schmid
Hannah Schmidt
Melissa Schmoll
Lennart Schneider
Xiao-Yin To
Viet Tran
Antje Völker
Moritz Wagner
Joshua Wagner
Maria Waize
Hannah Wecker
Rui Yang
Simone Zellner
Malte Nalenz
author_sort Heidi Seibold
title A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
title_short A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
title_full A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
title_fullStr A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
title_full_unstemmed A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.
title_sort computational reproducibility study of plos one articles featuring longitudinal data analyses.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/cc4ab404fc9743df8c8df776f92f6ace
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