论文标题
ABCMETAAPP：通过近似贝叶斯计算（ABC）基于模拟的均值估计和标准偏差的闪亮应用。
ABCMETAapp: R Shiny Application for Simulation-based Estimation of Mean and Standard Deviation for Meta-analysis via Approximate Bayesian Computation (ABC)
论文作者
论文摘要
背景和客观：在基于连续结果的荟萃分析中，所选研究的估计平均值和相应的标准偏差是获得平均值及其置信区间的汇总估计值的关键输入。我们经常遇到这些数量在文献中没有直接报道的情况。取而代之的是，报告了其他摘要统计数据，例如中值，最小，最大，四分位数和研究样本量。基于可用的摘要统计数据，我们需要估计荟萃分析的平均值和标准偏差。方法：我们基于近似贝叶斯计算（ABC）ABCMETA开发了闪亮的代码来处理这种情况。结果：在本文中，我们提出了一个交互式且用户友好的R闪亮应用程序，用于实现所提出的方法（命名为Abcmetaapp）。在Abcmetaapp中，当结果变量的分布偏斜或尾巴较重时，用户可以选择除正态分布以外的基本结果分布。我们展示了如何使用示例运行Abcmetaapp。结论：abcmetaapp提供了闪亮的实现。此方法比现有的分析方法更灵活，因为估计可以基于结果变量的五个不同分布（正常，lognormal，oppertental，thuper，weibull和beta）。
Background and Objective: In meta-analysis based on continuous outcome, estimated means and corresponding standard deviations from the selected studies are key inputs to obtain a pooled estimate of the mean and its confidence interval. We often encounter the situation that these quantities are not directly reported in the literatures. Instead, other summary statistics are reported such as median, minimum, maximum, quartiles, and study sample size. Based on available summary statistics, we need to estimate estimates of mean and standard deviation for meta-analysis. Methods: We developed a R Shiny code based on approximate Bayesian computation (ABC), ABCMETA, to deal with this situation. Results: In this article, we present an interactive and user-friendly R Shiny application for implementing the proposed method (named ABCMETAapp). In ABCMETAapp, users can choose an underlying outcome distribution other than the normal distribution when the distribution of the outcome variable is skewed or heavy tailed. We show how to run ABCMETAapp with examples. Conclusions: ABCMETAapp provides a R Shiny implementation. This method is more flexible than the existing analytical methods since estimation can be based on five different distribution (Normal, Lognormal, Exponential, Weibull, and Beta) for the outcome variable.
