Combined analysis of correlated data when data cannot be pooled
Combined analysis of correlated data when data cannot be pooled
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作者：
Jones, Elinor M.;Sheehan, Nuala A.;Burton, Paul
在遗传流行病学研究中，个体遗传变异与感兴趣的表型之间的关联通常很弱，需要大量样本来估计影响并解决复杂的统计问题。这样的样本量通常只能通过汇集多项研究的数据才能实现；然后可以通过对汇总数据进行个体级荟萃分析 (ILMA) 或进行传统的研究级荟萃分析 (SLMA) 来研究感兴趣的影响。然而，为 ILMA 汇集个人层面的研究数据并不总是可能的，研究人员可能被迫进行 SLMA，从而将共享限制为非公开的汇总统计数据。在某些情况下，可以在不汇集不同研究数据的情况下进行个体层面的分析。已经表明，当数据在研究之间水平划分时，即在每项研究中收集相同变量的数据但任何给定的研究参与者仅出现在一项研究中时，可以通过这种方式拟合广义线性模型。在本文中，我们证明可以通过类似的方式实现个体水平的广义估计方程元分析。这将无需数据池的 ILMA 范围扩展到涉及相关和集群响应的问题。版权所有 (C) 2013 约翰·威利父子有限公司
In genetic epidemiology studies, associations between individual genetic variants and phenotypes of interest are generally weak requiring large samples to estimate effects and to address complex statistical questions. Such sample sizes are often only achievable by pooling data from multiple studies; effects of interest can then be investigated through an individual-level meta-analysis (ILMA) on the pooled data, or by conducting a conventional study-level meta-analysis (SLMA). However, pooling individual-level research data for an ILMA is not always possible, and researchers may be compelled to conduct an SLMA instead, restricting the sharing to non-disclosing summary statistics. In certain settings, an individual-level analysis can be conducted without pooling the data from the different studies. It has already been shown that when data are horizontally partitioned between studies, i.e. data are collected on the same variables in each study but any given study participant appears in one study only, it is possible to fit a generalised linear model in this way. In the present paper, we demonstrate that an individual-level generalised estimating equations meta-analysis can be achieved in an analogous manner. This extends the scope of ILMA without data pooling to problems involving correlated and clustered responses. Copyright (C) 2013 John Wiley & Sons, Ltd.