Dehesh Tania, Zare Najaf, Ayatollahi Seyyed Mohammad Taghi
Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, P.O. Box 71345-1874, Shiraz, Iran.
Department of Biostatistics, Infertility Research Center, Shiraz University of Medical Sciences, P.O. Box 71345-1874, Shiraz, Iran.
Comput Math Methods Med. 2015;2015:801031. doi: 10.1155/2015/801031. Epub 2015 Sep 1.
Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach.
We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study.
Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC.
This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.
单变量荟萃分析（UM）程序作为一种提供单一总体结果的技术，越来越受欢迎。在模型中忽略其他伴随协变量的存在会导致治疗效率的损失。我们的目的是为系数协方差矩阵提出四种新的近似方法，而多元广义最小二乘法（MGLS）作为一种多元荟萃分析方法，不容易获得该协方差矩阵。
在一项模拟研究中，我们评估了四种新方法的效率，包括零相关（ZC）、共同相关（CC）、估计相关（EC）和多元多层相关（MMC），这些方法在合成Cox比例风险模型系数时对估计偏差、均方误差（MSE）和95%置信区间（CI）的概率覆盖率的影响。
对模拟研究中估计系数的MSE、偏差和CI结果进行比较表明，与EC、CC和ZC程序相比，MMC方法是最准确的程序。根据上述所有设置，四种方法的精度排名为MMC≥EC≥CC≥ZC。
本研究突出了MGLS荟萃分析相对于UM方法的优势。结果建议使用MMC程序来克服缺乏系数完整协方差矩阵信息的问题。
