Dissemination of novel biostatistics methods: Impact of programming code availability and other characteristics on article citations

• Published in: PloS one 2018-08, Vol.13 (8), p.e0201590-e0201590
• Main Authors: Wahlquist, Amy E, Muhammad, Lutfiyya N, Herbert, Teri Lynn, Ramakrishnan, Viswanathan, Nietert, Paul J
• Format: Article
• Language: English
• Subjects: Accessibility; Algorithms; Analysis; Bibliometrics; Biometricians; Biometrics; Biometry; Biostatistics; Citation analysis; Citation indexes; Computer and Information Sciences; Design; Health sciences; Identification methods; Internal medicine; Knowledge; Linear Models; Medicine; Methods; People and Places; Physical Sciences; Practice; Public health; Publications - classification; Regression analysis; Research and Analysis Methods; Researchers; Reviews; Scholarly publishing; Science Policy; Scientists; Scientometrics; Software; Statistical analysis; Statistical methods; Statistical models
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0201590

As statisticians develop new methodological approaches, there are many factors that influence whether others will utilize their work. This paper is a bibliometric study that identifies and quantifies associations between characteristics of new biostatistics methods and their citation counts. Of primary interest was the association between numbers of citations and whether software code was available to the reader. Statistics journal articles published in 2010 from 35 statistical journals were reviewed by two biostatisticians. Generalized linear mixed models were used to determine which characteristics (author, article, and journal) were independently associated with citation counts (as of April 1, 2017) in other peer-reviewed articles. Of 722 articles reviewed, 428 were classified as new biostatistics methods. In a multivariable model, for articles that were not freely accessible on the journal's website, having code available appeared to offer no boost to the number of citations (adjusted rate ratio = 0.96, 95% CI = 0.74 to 1.24, p = 0.74); however, for articles that were freely accessible on the journal's website, having code available was associated with a 2-fold increase in the number of citations (adjusted rate ratio = 2.01, 95% CI = 1.30 to 3.10, p = 0.002). Higher citation rates were also associated with higher numbers of references, longer articles, SCImago Journal Rank indicator (SJR), and total numbers of publications among authors, with the strongest impact on citation rates coming from SJR (rate ratio = 1.21 for a 1-unit increase in SJR; 95% CI = 1.11 to 1.32). These analyses shed new insight into factors associated with citation rates of articles on new biostatistical methods. Making computer code available to readers is a goal worth striving for that may enhance biostatistics knowledge translation.