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On the optimistic performance evaluation of newly introduced bioinformatic methods

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  • Buchka, Stefan
  • Hapfelmeier, Alexander
  • Gardner, Paul P
  • Wilson, Rory
  • Boulesteix, Anne-Laure

Abstract

Most research articles presenting new data analysis methods claim that “the new method performs better than existing methods”, but the veracity of such statements is questionable. Our manuscript discusses and illustrates consequences of the optimistic bias occurring during the evaluation of novel data analysis methods, that is, all biases resulting from, for example, selection of datasets or competing methods; better ability to fix bugs in a preferred method; and selective reporting of method variants. We quantitatively investigate this bias using a topical example from epigenetic analysis: normalization methods for data generated by the Illumina HumanMethylation450K BeadChip microarray.

Suggested Citation

  • 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.
    • Handle: RePEc:osf:metaar:pkqdx
    DOI: 10.31219/osf.io/pkqdx
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    References listed on IDEAS

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    1. Kenneth F Schulz & Douglas G Altman & David Moher & for the CONSORT Group, 2010. "CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomised Trials," PLOS Medicine, Public Library of Science, vol. 7(3), pages 1-7, March.
    2. 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.
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    Cited by:

    1. 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.

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