Daeyoung Lim, Ming-Hui Chen, Joseph G Ibrahim, Sungduk Kim, Arvind K Shah, Jianxin Lin
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引用次数: 0
摘要
元分析(Meta-analysis)是一种统计程序,它以一种有原则的方式对多项研究的结果进行比较、组合和综合,已在多个领域流行起来。由于数据的可用性和建模的灵活性,使用研究水平(或等同于汇总)数据进行元分析尤其受到关注。在本文中,我们介绍了一个 R 软件包 metapack,它为元分析和网络元分析引入了统一的公式界面。该用户界面以及该软件包允许对多元元分析模型和单变量网络元分析模型进行灵活的方差-协方差建模。这些模型的复杂计算阻碍了它们的广泛应用。该软件包还提供了生成相关图表和执行统计推断(如模型评估)的功能。使用 metapack 中包含的两个真实数据集演示了使用案例。
metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface.
Meta-analysis, a statistical procedure that compares, combines, and synthesizes research findings from multiple studies in a principled manner, has become popular in a variety of fields. Meta-analyses using study-level (or equivalently aggregate) data are of particular interest due to data availability and modeling flexibility. In this paper, we describe an R package metapack that introduces a unified formula interface for both meta-analysis and network meta-analysis. The user interface-and therefore the package-allows flexible variance-covariance modeling for multivariate meta-analysis models and univariate network meta-analysis models. Complicated computing for these models has prevented their widespread adoption. The package also provides functions to generate relevant plots and perform statistical inferences like model assessments. Use cases are demonstrated using two real data sets contained in metapack.
R JournalCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍:
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.
The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to:
- put their contribution in context, in particular discuss related R functions or packages;
- explain the motivation for their contribution;
- provide code examples that are reproducible.