On the relative efficiency of using summary statistics versus individual-level data in meta-analysis
On the relative efficiency of using summary statistics versus individual-level data in meta-analysis
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DOI：
10.1093/biomet/asq006
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发表时间：
2010-06-01
期刊：
影响因子：
2.7
通讯作者：
Zeng, D.
中科院分区：
文献类型：
--
作者：
Lin, D. Y.;Zeng, D.
荟萃分析被广泛用于综合多项研究的结果。虽然元分析传统上是通过结合相关研究的汇总统计数据进行的，但技术和通信的进步使访问单个参与者的原始数据变得越来越可行。在本文中，我们调查的相对效率分析原始数据相结合的汇总统计。我们发现，对于所有常用的参数和半参数模型，如果主要关注的参数在研究中有一个共同的值，讨厌的参数在研究中有不同的值，并且汇总统计量基于最大似然法，则通过分析原始数据没有渐近效率增益。我们还评估了这两种方法的相对效率时，主要利益的参数有不同的值之间的研究，或当有共同的滋扰参数跨研究。我们进行模拟研究，以确认理论结果，并提供经验比较的遗传关联研究。
Meta-analysis is widely used to synthesize the results of multiple studies. Although meta-analysis is traditionally carried out by combining the summary statistics of relevant studies, advances in technologies and communications have made it increasingly feasible to access the original data on individual participants. In the present paper, we investigate the relative efficiency of analyzing original data versus combining summary statistics. We show that, for all commonly used parametric and semiparametric models, there is no asymptotic efficiency gain by analyzing original data if the parameter of main interest has a common value across studies, the nuisance parameters have distinct values among studies, and the summary statistics are based on maximum likelihood. We also assess the relative efficiency of the two methods when the parameter of main interest has different values among studies or when there are common nuisance parameters across studies. We conduct simulation studies to confirm the theoretical results and provide empirical comparisons from a genetic association study.