A systematic review finds that spin or interpretation bias is abundant in evaluations of ovarian cancer biomarkers.


Journal

Journal of clinical epidemiology
ISSN: 1878-5921
Titre abrégé: J Clin Epidemiol
Pays: United States
ID NLM: 8801383

Informations de publication

Date de publication:
12 2019
Historique:
received: 29 10 2018
revised: 20 06 2019
accepted: 15 07 2019
pubmed: 23 7 2019
medline: 22 5 2020
entrez: 23 7 2019
Statut: ppublish

Résumé

In the scientific literature, "spin" refers to reporting practices that make the study findings appear more favorable than results justify. The practice of "spin" or misrepresentation and overinterpretation may lead to an imbalanced and unjustified optimism in the interpretation of study results about performance of putative biomarkers. We aimed to classify spin (i.e., misrepresentation and overinterpretation of study findings) in recent clinical studies evaluating the performance of biomarkers in ovarian cancer. We searched PubMed systematically for all evaluations of ovarian cancer biomarkers published in 2015. Studies eligible for inclusion reported the clinical performance of prognostic, predictive, or diagnostic biomarkers. Our search identified 1,026 studies; 326 studies met all eligibility criteria, of which we evaluated the first 200 studies. Of these, 140 (70%) contained one or more form of spin in the title, abstract, or main-text conclusion, exaggerating the performance of the biomarker. 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%). Our study provides evidence of misrepresentation and overinterpretation of finding in recent clinical evaluations of ovarian cancer biomarkers.

Sections du résumé

BACKGROUND
In the scientific literature, "spin" refers to reporting practices that make the study findings appear more favorable than results justify. The practice of "spin" or misrepresentation and overinterpretation may lead to an imbalanced and unjustified optimism in the interpretation of study results about performance of putative biomarkers. We aimed to classify spin (i.e., misrepresentation and overinterpretation of study findings) in recent clinical studies evaluating the performance of biomarkers in ovarian cancer.
METHODS
We searched PubMed systematically for all evaluations of ovarian cancer biomarkers published in 2015. Studies eligible for inclusion reported the clinical performance of prognostic, predictive, or diagnostic biomarkers.
RESULTS
Our search identified 1,026 studies; 326 studies met all eligibility criteria, of which we evaluated the first 200 studies. Of these, 140 (70%) contained one or more form of spin in the title, abstract, or main-text conclusion, exaggerating the performance of the biomarker. 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%).
CONCLUSION
Our study provides evidence of misrepresentation and overinterpretation of finding in recent clinical evaluations of ovarian cancer biomarkers.

Identifiants

pubmed: 31330251
pii: S0895-4356(18)30952-1
doi: 10.1016/j.jclinepi.2019.07.011
pii:
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Review Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

9-17

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.

Auteurs

Mona Ghannad (M)

Amsterdam UMC, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands; Université de Paris, CRESS, INSERM, INRA, F-75004 Paris, France. Electronic address: m.ghannad@amc.uva.nl.

Maria Olsen (M)

Amsterdam UMC, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands; Université de Paris, CRESS, INSERM, INRA, F-75004 Paris, France.

Isabelle Boutron (I)

Université de Paris, CRESS, INSERM, INRA, F-75004 Paris, France.

Patrick M Bossuyt (PM)

Amsterdam UMC, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, University of Amsterdam, Amsterdam Public Health Research Institute, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.

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