College of Mathematics, 12530Sichuan University, Chengdu, Sichuan, China.
Med-X Center for Informatics, 12530Sichuan University, Chengdu, Sichuan, China.
Stat Methods Med Res. 2021 Dec;30(12):2701-2719. doi: 10.1177/09622802211047348. Epub 2021 Oct 20.
In recent years, a growing number of researchers have attempted to overcome the constraints of size and scope in different medical studies to find out the overall treatment effects. As a widespread technique to combine results of multiple studies, commonly used meta-analytic approaches for continuous outcomes demand sample means and standard deviations of primary studies, which are absent sometimes, especially when the outcome is skewed. Instead, the median, the extrema, and/or the quartiles are reported. One feasible solution is to convert the preceding order statistics to demanded statistics to keep effect measures consistent. In this article, we propose new methods based on maximum likelihood estimation for known distributions with unknown parameters. For unknown underlying distributions, the Box-Cox transformation is applied to the reported order statistics so that the techniques for normal distribution can be utilized. Two approaches for estimating the power parameter in Box-Cox transformation are provided. Both simulation studies and real data analysis indicate that in most cases, the proposed methods outperform the existing methods in estimation accuracy.
近年来，越来越多的研究人员试图克服不同医学研究中大小和范围的限制，以找出总体治疗效果。作为一种广泛使用的组合多项研究结果的技术，常用的连续结局荟萃分析方法需要原始研究的样本均值和标准差，但有时这些数据是缺失的，特别是当结果偏态分布时。相反，中位数、极值和/或四分位数被报告。一种可行的解决方案是将前面的顺序统计量转换为所需的统计量，以保持效应量的一致性。在本文中，我们提出了基于最大似然估计的新方法，用于具有未知参数的已知分布。对于未知的潜在分布，应用 Box-Cox 变换到报告的顺序统计量，以便可以利用正态分布的技术。提供了两种估计 Box-Cox 变换中幂参数的方法。模拟研究和实际数据分析都表明，在大多数情况下，所提出的方法在估计准确性方面优于现有方法。
