Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States of America.
Department of Library Science and Informatics, Medical University of South Carolina, Charleston, SC, United States of America.
PLoS One. 2018 Aug 1;13(8):e0201590. doi: 10.1371/journal.pone.0201590. eCollection 2018.
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.
随着统计学家开发新的方法，有许多因素会影响其他人是否会利用他们的工作。本文是一项文献计量研究，旨在确定和量化新生物统计学方法的特征与其引用次数之间的关联。主要关注的是引用次数与读者是否可以获得软件代码之间的关联。
两位生物统计学家对 2010 年发表在 35 种统计期刊上的统计学期刊文章进行了回顾。使用广义线性混合模型来确定哪些特征（作者、文章和期刊）与其他同行评审文章的引用次数（截至 2017 年 4 月 1 日）独立相关。
在回顾的 722 篇文章中，有 428 篇被归类为新生物统计学方法。在多变量模型中，对于在期刊网站上无法免费获取的文章，提供代码似乎并没有增加引用次数（调整后的比率比=0.96，95%置信区间=0.74 至 1.24，p=0.74）；然而，对于在期刊网站上可以免费获取的文章，提供代码与引用次数增加 2 倍相关（调整后的比率比=2.01，95%置信区间=1.30 至 3.10，p=0.002）。更高的引用率也与更多的参考文献、更长的文章、SCImago 期刊排名指标（SJR）以及作者的总出版物数量相关，其中对引用率影响最大的是 SJR（每增加 1 个单位，比率比=1.21；95%置信区间=1.11 至 1.32）。
这些分析为新生物统计学方法文章的引用率相关因素提供了新的见解。向读者提供计算机代码是值得努力的目标，这可能会增强生物统计学知识的转化。
