How should meta-regression analyses be undertaken and interpreted?
How should meta-regression analyses be undertaken and interpreted?
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DOI：
10.1002/sim.1187
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发表时间：
2002-06-15
影响因子：
2
通讯作者：
Higgins, JPT
中科院分区：
文献类型：
--
作者：
Thompson, SG;Higgins, JPT
应用于一系列临床试验的元回归的适当方法以及解释的局限性和陷阱尚未得到充分认识。在这里，我们总结了最近针对这些问题的研究，并根据这项工作考虑了三个已发表的元回归示例。一个主要的方法论问题是，荟萃回归应该进行加权，以考虑治疗效果的试验内方差和试验间残留的异质性（即回归中协变量无法解释的异质性）。这对应于随机效应元回归。从元回归中得出的关联是观察性的，并且比从随机比较中得出的因果关系具有更弱的解释。当每次试验中患者特征的平均值用作回归中的协变量时，这一点尤其适用。数据挖掘是从元回归中得出可靠结论的主要陷阱。它只能通过预先指定协变量来避免，这些协变量将作为异质性的潜在来源进行研究。然而，在实践中，这并不总是容易实现。本文考虑的例子显示了使用元回归的科学原理与此类分析容易出现的困难解释问题之间的紧张关系。版权所有 (C) 2002 John Wiley Sons, Ltd.
Appropriate methods for meta-regression applied to a set of clinical trials, and the limitations and pitfalls in interpretation, are insufficiently recognized. Here we summarize recent research focusing on these issues, and consider three published examples of meta-regression in the light of this work. One principal methodological issue is that meta-regression should be weighted to take account of both within-trial variances of treatment effects and the residual between-trial heterogeneity (that is, heterogeneity not explained by the covariates in the regression). This corresponds to random effects meta-regression. The associations derived from meta-regress ions are observational, and have a weaker interpretation than the causal relationships derived from randomized comparisons. This applies particularly when averages of patient characteristics in each trial are used as covariates in the regression. Data dredging is the main pitfall in reaching reliable conclusions from meta-regression. It can only be avoided by prespecification of covariates that will be investigated as potential sources of heterogeneity. However, in practice this is not always easy to achieve. The examples considered in this paper show the tension between the scientific rationale for using meta-regression and the difficult interpretative problems to which such analyses are prone. Copyright (C) 2002 John Wiley Sons, Ltd.