Zhang Zhongheng, Kossmeier Michael, Tran Ulrich S, Voracek Martin, Zhang Haoyang
Department of Emergency Medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Department of Basic Psychological Research and Research Methods, School of Psychology, University of Vienna, Vienna, Austria.
Ann Transl Med. 2017 Dec;5(24):485. doi: 10.21037/atm.2017.10.07.
While the conventional forest plot is useful to present results within subgroups of patients in clinical studies, it has been criticized for several reasons. First, small subgroups are visually overemphasized by long confidence interval lines, which is misleading. Second, the point estimates of large subgroups are difficult to discern because of the large box representing the precision of the estimate within subgroups. Third, confidence intervals depicted by lines might incorrectly convey the impression that all points within the interval are equally likely. Rainforest plots have been proposed to overcome these potentially misleading aspects of conventional forest plots. The package enables to generate rainforest plots for meta-analysis within the statistical computing environment R. We suggest the application of rainforest plots for the depiction of subgroup analysis in clinical trials. In this tutorial, detailed step-by-step guidance on the generation of rainforest plot for this purpose is provided.
虽然传统的森林图有助于在临床研究的患者亚组中呈现结果，但它因多种原因而受到批评。首先，小亚组会被长置信区间线在视觉上过度强调，这具有误导性。其次，由于代表亚组内估计精度的大框，大亚组的点估计难以辨别。第三，用线条描绘的置信区间可能会错误地传达区间内所有点可能性均等的印象。为克服传统森林图这些潜在的误导性方面，人们提出了雨林图。该软件包能够在统计计算环境R中生成用于荟萃分析的雨林图。我们建议在临床试验中应用雨林图来描绘亚组分析。在本教程中，将提供为此目的生成雨林图的详细分步指南。
