A general framework for the use of logistic regression models in meta-analysis
A general framework for the use of logistic regression models in meta-analysis
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DOI：
10.1177/0962280214534409
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发表时间：
2016-12-01
影响因子：
2.3
通讯作者：
Higgins, Julian P. T.
中科院分区：
文献类型：
--
作者：
Simmonds, Mark C.;Higgins, Julian P. T.
在二分事件结局的荟萃分析中，每个随机试验的个体参与者数据都是可用的，“一阶段”随机效应logistic回归模型已被提出作为分析这些数据的一种方法。即使在无法获得个体参与者数据且我们只有汇总列联表数据的情况下，也可以使用此类模型。与传统的荟萃分析方法相比，这种一阶段回归模型的一个好处是，它最大化了数据的正确二项式似然，因此不需要效应估计呈正态分布的常见假设。使用该模型的第二个好处是，它可以应用，只有微小的修改，在一系列的元分析的情况下，包括元回归，网络元分析和诊断测试准确性的元分析。这个单一的模型可以潜在地取代在这些领域中使用的各种通常复杂的方法。本文认为，一系列的荟萃分析的例子，如何随机效应的逻辑回归模型可能会被用于一些不同类型的荟萃分析。这一阶段的方法进行了比较，广泛使用的荟萃分析方法，包括贝叶斯网络荟萃分析和双变量和分层汇总受试者工作特征（ROC）模型的诊断测试准确性的荟萃分析。
Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage'' random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy.