High-throughput analysis suggests differences in journal false discovery rate by subject area and impact factor but not open access status

• Published in: BMC bioinformatics 2020-12, Vol.21 (1), p.564-564, Article 564
• Main Authors: Hall, L M, Hendricks, A E
• Format: Article
• Language: English
• Subjects: Analysis; Biology; Cancer; Empirical analysis; False discovery rate; Humans; Impact factor; Impact factors; Journal Impact Factor; Medical research; Medicine; Methods; Oncology; Oncology, Experimental; Open access; Open access journals; Open Access Publishing; Peer review; Periodicals as Topic; Replication; Reproducibility; Statistical analysis
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-03817-7

A low replication rate has been reported in some scientific areas motivating the creation of resource intensive collaborations to estimate the replication rate by repeating individual studies. The substantial resources required by these projects limits the number of studies that can be repeated and consequently the generalizability of the findings. We extend the use of a method from Jager and Leek to estimate the false discovery rate for 94 journals over a 5-year period using p values from over 30,000 abstracts enabling the study of how the false discovery rate varies by journal characteristics. We find that the empirical false discovery rate is higher for cancer versus general medicine journals (p = 9.801E-07, 95% CI: 0.045, 0.097; adjusted mean false discovery rate cancer = 0.264 vs. general medicine = 0.194). We also find that false discovery rate is negatively associated with log journal impact factor. A two-fold decrease in journal impact factor is associated with an average increase of 0.020 in FDR (p = 2.545E-04). Conversely, we find no statistically significant evidence of a higher false discovery rate, on average, for Open Access versus closed access journals (p = 0.320, 95% CI - 0.015, 0.046, adjusted mean false discovery rate Open Access = 0.241 vs. closed access = 0.225). Our results identify areas of research that may need additional scrutiny and support to facilitate replicable science. Given our publicly available R code and data, others can complete a broad assessment of the empirical false discovery rate across other subject areas and characteristics of published research.