Oncology trial abstracts showed suboptimal improvement in reporting: a comparative before-and-after evaluation using CONSORT for Abstract guidelines

J Clin Epidemiol. 2014 Jun;67(6):658-66. doi: 10.1016/j.jclinepi.2013.10.012. Epub 2014 Jan 16.


Objectives: The aims of this study were to evaluate the quality of randomized controlled trial (RCT) abstracts published in the field of oncology and identify characteristics associated with better reporting quality.

Study design and setting: All phase III trials published during 2005-2007 [before Consolidated Standards of Reporting Trials (CONSORT)] and 2010-2012 (after CONSORT) were searched electronically in MEDLINE/PubMed and retrieved for review using an 18-point overall quality score (OQS) for reporting based on the CONSORT for Abstract guidelines. Descriptive statistics followed by multivariate linear regression were used to identify features associated with improved reporting quality.

Results: The mean OQS was 8.2 (range: 5-13; 95% confidence interval (CI): 8.0, 8.3) and 9.9 (range: 5-18; 95% CI: 9.7, 10.2) in the pre- and post-CONSORT periods, respectively. The method for random sequence generation, allocation concealment, blinding details, and funding sources were missing in pre-CONSORT abstracts and insufficiently reported (<20%) in post-CONSORT abstracts. A high impact factor (P < 0.001) and the journal of publication (P < 0.001) were independent factors that were significantly associated with higher reporting quality on multivariate analysis.

Conclusion: The reporting quality of RCT abstracts in oncology showed suboptimal improvement over time. Thus, stricter adherence to the CONSORT for Abstract guidelines is needed to improve the reporting quality of RCT abstracts published in oncology.

Keywords: Bias; CONSORT for Abstracts; Oncology; Randomized trials; Reporting quality; Research design.

Publication types

  • Comparative Study
  • Review

MeSH terms

  • Abstracting and Indexing / standards*
  • Clinical Trials, Phase III as Topic*
  • Guidelines as Topic
  • Humans
  • Linear Models
  • Medical Oncology*
  • Multivariate Analysis
  • Quality Improvement / statistics & numerical data*
  • Randomized Controlled Trials as Topic*