Chol dataset.

In meta-analysis literature, there are several checklists describing the procedures necessary to evaluate studies from a qualitative point of view, whereas preliminary quantitative and statistical investigations on the “combinability” of trials have been neglected. Covariate balance is an important prerequisite to conduct meta-analysis. We propose a method to identify unbalanced trials with respect to a set of covariates, in presence of covariate imbalance, namely when the randomized controlled trials generate a meta-sample that cannot satisfy the requisite of randomization/combinability in meta-analysis. The method is able to identify the unbalanced trials, through four stages aimed at achieving combinability. The studies responsible for the imbalance are identified, and then they can be eliminated. The proposed procedure is simple and relies on the combined Anderson-Darling test applied to the Empirical Cumulative Distribution Functions of both experimental and control meta-arms. To illustrate the method in practice, two datasets from well-known meta-analyses in the literature are used.

在元分析（meta-analysis）相关文献中，已有诸多检查表阐述了从定性视角评估研究所需的操作流程，但针对试验"可合并性"的初步定量与统计研究却长期被忽视。协变量平衡（covariate balance）是开展元分析的重要前提条件。本文提出一种针对一组协变量识别失衡试验的方法，适用于协变量失衡场景：即当随机对照试验（randomized controlled trial, RCT）生成的元样本无法满足元分析的随机化与可合并性要求时，该方法可有效完成失衡试验识别。该方法通过四个旨在实现试验可合并性的阶段开展工作，先定位导致协变量失衡的研究，随后可将其剔除。所提流程简便易行，核心依托于分别应用于元分析试验组与对照组经验累积分布函数（Empirical Cumulative Distribution Function, ECDF）的组合安德森-达令检验（Anderson-Darling test）。为演示该方法的实际应用效果，本文选用了文献中两篇经典元分析对应的数据集作为示例。