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A Plea for Neutral Comparison Studies in Computational Sciences

Author

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  • Anne-Laure Boulesteix
  • Sabine Lauer
  • Manuel J A Eugster

Abstract

: In computational science literature including, e.g., bioinformatics, computational statistics or machine learning, most published articles are devoted to the development of “new methods”, while comparison studies are generally appreciated by readers but surprisingly given poor consideration by many journals. This paper stresses the importance of neutral comparison studies for the objective evaluation of existing methods and the establishment of standards by drawing parallels with clinical research. The goal of the paper is twofold. Firstly, we present a survey of recent computational papers on supervised classification published in seven high-ranking computational science journals. The aim is to provide an up-to-date picture of current scientific practice with respect to the comparison of methods in both articles presenting new methods and articles focusing on the comparison study itself. Secondly, based on the results of our survey we critically discuss the necessity, impact and limitations of neutral comparison studies in computational sciences. We define three reasonable criteria a comparison study has to fulfill in order to be considered as neutral, and explicate general considerations on the individual components of a “tidy neutral comparison study”. R codes for completely replicating our statistical analyses and figures are available from the companion website http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/plea2013.

Suggested Citation

  • Anne-Laure Boulesteix & Sabine Lauer & Manuel J A Eugster, 2013. "A Plea for Neutral Comparison Studies in Computational Sciences," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-11, April.
    • Handle: RePEc:plo:pone00:0061562
    DOI: 10.1371/journal.pone.0061562
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    Citations

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    Cited by:

    1. Christian Hennig, 2022. "An empirical comparison and characterisation of nine popular clustering methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 201-229, March.
    2. Efthymios Costa & Ioanna Papatsouma & Angelos Markos, 2023. "Benchmarking distance-based partitioning methods for mixed-type data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 701-724, September.
    3. Buchka, Stefan & Hapfelmeier, Alexander & Gardner, Paul P & Wilson, Rory & Boulesteix, Anne-Laure, 2021. "On the optimistic performance evaluation of newly introduced bioinformatic methods," MetaArXiv pkqdx, Center for Open Science.
    4. Freuli, Francesca & Held, Leonhard & Heyard, Rachel, 2022. "Replication success under questionable research practices – a simulation study," MetaArXiv s4b65, Center for Open Science.
    5. Theresa Ullmann & Anna Beer & Maximilian Hünemörder & Thomas Seidl & Anne-Laure Boulesteix, 2023. "Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(1), pages 211-238, March.
    6. Freuli, Francesca & Held, Leonhard & Heyard, Rachel, 2022. "Replication Success under Questionable Research Practices - A Simulation Study," I4R Discussion Paper Series 2, The Institute for Replication (I4R).
    7. Anne-Laure Boulesteix & Robert Hable & Sabine Lauer & Manuel J. A. Eugster, 2015. "A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 201-212, August.

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