41 Misrepresentation and overinterpretation in evaluations of biomarkers in ovarian cancer: a systematic review

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We aimed to (1) document and classify spin (i.e., misrepresentation and overinterpretation of study findings in the title, abstract, and main text conclusion, exaggerating the performance of the biomarker), and (2) facilitators of spin (i.e., practices that would facilitate overinterpretation of results), in recent clinical studies evaluating the performance of biomarkers in ovarian cancer.


We searched PubMed systematically for all evaluations of biomarkers in ovarian cancer published in 2015. Studies eligible for inclusion reported the clinical performance of prognostic, predictive, or diagnostic biomarkers. Reviews, animal studies, and cell line studies were excluded. All studies were independently screened by two reviewers. To document and characterise spin, we developed a set of scoring criteria with two reviewers.


In total, 1026 citations were retrieved by our search strategy; 326 studies met all eligibility criteria, of which the first 200 studies, when ranked according to publication date, were included in our analysis. One-third (60; 30%) of studies were free of spin, one-third (65; 32.5%) contained one type of spin, and another third (75; 38%) contained two or more forms of spin in the article. Spin was classified into two categories: (1) misrepresentation, (2) misinterpretation. The most frequent forms of spin identified were: (1) other purposes of biomarker claimed not investigated (65; 32.5%); (2) mismatch between intended aim and conclusion (57; 28.5%); and (3) incorrect presentation of results (40; 20%). Frequently observed facilitators of spin were: (1) not stating sample size calculations (200; 100%); (2) not mentioning potential harms (200; 100%); and (3) not pre-specifying a positivity threshold for a continuous biomarker (84 of 164 studies; 51.2%).


Reports of studies evaluating the clinical performance of biomarkers in ovarian cancer frequently have spin. Misinterpretation and misrepresentation of biomarker performance may account for a considerable amount of waste in the biomarker discovery process. Strategies to curb inflated and biassed reporting are needed to improve the quality and credibility of published biomarker studies.

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