An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown
An alternative model for bivariate random-effects meta-analysis when the within-study correlations are unknown
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DOI：
10.1093/biostatistics/kxm023
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发表时间：
2008-01-01
期刊：
影响因子：
2.1
通讯作者：
Abrams, Keith R.
中科院分区：
文献类型：
--
作者：
Riley, Richard D.;Thompson, John R.;Abrams, Keith R.
多变量荟萃分析模型可用于综合多个相关终点，如总生存期和无病生存期。多变量随机效应荟萃分析的层次框架包括研究内和研究间的相关性。假设研究内部的相关性是已知的，但它们通常是不可用的，这限制了实践中的多变量方法。在本文中，我们考虑了两个相关终点的综合，并提出了双变量随机效应荟萃分析（BRMA）的替代模型。该模型在分析中保留了每个研究的单独权重，但只包括一个总体相关参数rho，这消除了了解研究内部相关性的需要。此外，拟合模型所需的唯一数据是对每个终点进行单独的单变量随机效应元分析（URMA）所需的数据，这是目前实践中常用的方法。这使得替代模型立即适用于各种证据合成情况，包括预后和替代结果的研究。我们通过分析评估、现实模拟研究和对文献数据集的应用来检验替代模型的性能。我们的研究结果表明，除非（rho） over cap非常接近1或-1，否则替代模型产生适当的汇总估计，偏差很小，(i)与完全分层BRMA模型非常相似，其中研究内相关性是已知的；（ii）具有比单独urma更好的统计特性，特别是在缺少数据的情况下。替代模型也比完全分层模型更不容易在参数空间边界上进行估计，因此即使在已知研究内相关性的情况下也可能是首选模型。它还适当地估计了汇总估计及其相关性的函数；然而，它只提供了研究间差异的近似指示。替代模型极大地促进了meta分析中相关性的利用，并且应该允许BRMA在实践中的更多应用。
Multivariate meta-analysis models can be used to synthesize multiple, correlated endpoints such as overall and disease-free survival. A hierarchical framework for multivariate random-effects meta-analysis includes both within-study and between-study correlation. The within-study correlations are assumed known, but they are usually unavailable, which limits the multivariate approach in practice. In this paper, we consider synthesis of 2 correlated endpoints and propose an alternative model for bivariate random-effects meta-analysis (BRMA). This model maintains the individual weighting of each study in the analysis but includes only one overall correlation parameter, rho, which removes the need to know the within-study correlations. Further, the only data needed to fit the model are those required for a separate univariate random-effects meta-analysis (URMA) of each endpoint, currently the common approach in practice. This makes the alternative model immediately applicable to a wide variety of evidence synthesis situations, including studies of prognosis and surrogate outcomes. We examine the performance of the alternative model through analytic assessment, a realistic simulation study, and application to data sets from the literature. Our results show that, unless (rho) over cap is very close to 1 or -1, the alternative model produces appropriate pooled estimates with little bias that (i) are very similar to those from a fully hierarchical BRMA model where the within-study correlations are known and (ii) have better statistical properties than those from separate URMAs, especially given missing data. The alternative model is also less prone to estimation at parameter space boundaries than the fully hierarchical model and thus may be preferred even when the within-study correlations are known. It also suitably estimates a function of the pooled estimates and their correlation; however, it only provides an approximate indication of the between-study variation. The alternative model greatly facilitates the utilization of correlation in meta-analysis and should allow an increased application of BRMA in practice.