Ten Simple Rules for Reducing Overoptimistic Reporting in Methodological Computational Research

By Anne-Laure Boulesteix. 2015

59 citations recorded by the source corpus.

In most scientific fields, and in biomedical research in particular, there have long been many discussions on how to improve research practices and methods. The trend has increased in recent years, as illustrated by the series on “reducing waste,” published in The Lancet in January 2014 [1], or by the recent essay by John Ioannidis on how to make published results more true [2], which echoes his earlier provocative paper entitled “Why most published research findings are false” [3]. One of the important aspects underlying these discussions is that biomedical literature is most often overoptimistic with respect to, for example, the superiority of a new therapy or the strength of association between a risk factor and an outcome. Published results appear more significant, more spectacular, or sometimes more intuitive—in a word, more “satisfactory”—to authors and readers than they actually would if they reflected the truth. Causes of this problem are diverse, numerous, and interrelated. The effects of “fishing for significance” strategies or selective/incomplete reporting are exacerbated by design issues (e.g., small sample sizes, many investigated features) [3] or publication bias [4], to cite only a few of the factors at work. Research and guidelines on how to reduce overoptimistic reporting in the context of computational research, including computational biology as an important special case, however, are surprisingly scarce. Many methodological articles published in computational literature report the (vastly) superior performance of new methods [5], too often in general terms and—directly or indirectly—implying that the presented positive results are generalizable to other settings. Such overoptimistic reporting confuses readers, makes literature less credible and more difficult to interpret, and might even ultimately lead to a waste of resources in some cases. Here I take advantage of the popular “ten-simple-rules” format [6] to address the problem of overoptimistic reporting in methodological computational biology research, that is papers—termed “methodological papers” here—devoted primarily to the development and testing of new computational methods (intended to be used by other researchers on other data in the future) rather than to the biological question itself or the specific dataset at hand.

Subfields: Artificial Intelligence, Information Systems and Management

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Citations in the corpus

PotentialNet for Molecular Property Prediction (2018, background): “Such sensitivity underscores the criticality of rigorous cross-validation 35,36 .”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “Correct validation procedures are needed [113], with assessment of calibration and clinical utility in addition to discrimination, to deﬁne situations where modern methods have advantages over traditional approaches.”
Ten quick tips for machine learning in computational biology (2017, methodology): “In fact, a common mistake in machine learning is using, in the test set, data instances already used during the training phase or the hyper-parameter optimization phase, and then obtaining inflated performance scores [15].”
Random forest versus logistic regression: a large-scale benchmark experiment (2018, background): “Therefore, “fishing for datasets” after completion of the benchmark experiment should be prohibited, see Rule 4 of the “ten simple rules for reducing over-optimistic reporting” [28].”