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

Author

Listed:
  • 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

Abstract

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.

Suggested Citation

  • 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 Ma, 2021. "A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.
    • Handle: RePEc:plo:pone00:0251194
    DOI: 10.1371/journal.pone.0251194
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    References listed on IDEAS

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    1. Jessica L Couture & Rachael E Blake & Gavin McDonald & Colette L Ward, 2018. "A funder-imposed data publication requirement seldom inspired data sharing," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-13, July.
    2. Kevin V Lemley & Serena M Bagnasco & Cynthia C Nast & Laura Barisoni & Catherine M Conway & Stephen M Hewitt & Peter X K Song, 2016. "Morphometry Predicts Early GFR Change in Primary Proteinuric Glomerulopathies: A Longitudinal Cohort Study Using Generalized Estimating Equations," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
    3. Esther Maassen & Marcel A L M van Assen & Michèle B Nuijten & Anton Olsson-Collentine & Jelte M Wicherts, 2020. "Reproducibility of individual effect sizes in meta-analyses in psychology," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-18, May.
    4. repec:nas:journl:v:115:y:2018:p:2584-2589 is not listed on IDEAS
    5. Maria Vivien Visaya & David Sherwell & Benn Sartorius & Fabien Cromieres, 2015. "Analysis of Binary Multivariate Longitudinal Data via 2-Dimensional Orbits: An Application to the Agincourt Health and Socio-Demographic Surveillance System in South Africa," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-27, April.
    6. Martí Casals & Montserrat Girabent-Farrés & Josep L Carrasco, 2014. "Methodological Quality and Reporting of Generalized Linear Mixed Models in Clinical Medicine (2000–2012): A Systematic Review," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-10, November.
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