Literature DB >> 31588779

Comparison between protocols and publications for prognostic and predictive cancer biomarker studies.

Adelaide Doussau1, Esther Vinarov1, Brianna Barsanti-Innes1, Jonathan Kimmelman1.   

Abstract

BACKGROUND: Method prespecification in study protocols is important for controlling bias in reports. The primary goal of this study was to assess potential for discordance between study protocols and publications reporting predictive or prognostic cancer biomarker research. Secondary objectives included comparing characteristics of publications with accessible protocols compared to those without.
METHODS: Publications reporting predictive or prognostic cancer biomarker research were identified from 15 major journals, 2012-2015. Protocols were sought online or through repeated queries of corresponding authors. The following four items were extracted: (1) biomarkers, (2) biospecimen/assays, (3) sample size, (4) endpoints. We defined "explicit discordance" as the presence of major inconsistencies on these items.
RESULTS: Of 149 eligible publications, we obtained 19 eligible protocols online (13%). Out of a random sample of 103 publications where protocols were not available online, 12 protocols (12%) were furnished by corresponding authors; 8 (8% of authors) explicitly stated the absence of a protocol. Among 24 retrospective cohort studies, no protocol could be accessed. We found explicit discordance between publications and protocols for 18 studies (58%), in particular choice of biomarkers (36%), biospecimen/assays (6%), or endpoints (29%).
CONCLUSION: Protocols are generally not accessible or not used for cancer biomarker studies. Publications were often explicitly discordant with protocols, particularly regarding biomarkers and endpoints. Our findings point to common unaddressed risk of bias in publications of major journals reporting the relationship between cancer biomarkers and clinical endpoints.

Entities:  

Keywords:  Ethics; cancer; precision medicine; protocol; randomized trials

Year:  2019        PMID: 31588779     DOI: 10.1177/1740774519876912

Source DB:  PubMed          Journal:  Clin Trials        ISSN: 1740-7745            Impact factor:   2.486


  3 in total

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Review 2.  Structured reporting to improve transparency of analyses in prognostic marker studies.

Authors:  Willi Sauerbrei; Tim Haeussler; James Balmford; Marianne Huebner
Journal:  BMC Med       Date:  2022-05-12       Impact factor: 11.150

3.  Clinical trial transparency and data sharing among biopharmaceutical companies and the role of company size, location and product type: a cross-sectional descriptive analysis.

Authors:  Sydney Axson; Michelle M Mello; Deborah Lincow; Catherine Yang; Cary Gross; Joseph S Ross; Jennifer Miller
Journal:  BMJ Open       Date:  2021-07-19       Impact factor: 2.692

  3 in total

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