SummaryMeta-analysis has become a powerful tool for improving inference by gathering evidence from multiple sources. It pools summary-level data from different studies to improve estimation efficiency with the assumption that all participating studies are analysed under the same statistical model. It is challenging to integrate external summary data calculated from different models with a newly conducted internal study in which individual-level data are collected. We develop a novel statistical inference framework that can effectively synthesize internal and external data for the integrative analysis. The new framework is versatile enough to assimilate various types of summary data from multiple sources. We establish asymptotic properties for the proposed procedure and prove that the new estimate is theoretically more efficient than the internal data based maximum likelihood estimate, as well as a recently developed constrained maximum likelihood approach that incorporates the external information. We illustrate an application of our method by evaluating cervical cancer risk using data from a large cervical screening program.

中文翻译：

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通过利用外部汇总数据改进统计推断的广义集成模型

总结 Meta 分析已成为通过从多个来源收集证据来改进推理的强大工具。它汇集了来自不同研究的汇总级数据，以提高估计效率，并假设所有参与的研究都在相同的统计模型下进行分析。将根据不同模型计算的外部汇总数据与新进行的收集个人级别数据的内部研究相结合是具有挑战性的。我们开发了一种新颖的统计推理框架，可以有效地综合内部和外部数据进行综合分析。新框架的通用性足以吸收来自多个来源的各种类型的摘要数据。我们为所提出的程序建立渐近特性，并证明新估计在理论上比基于内部数据的最大似然估计以及最近开发的结合外部信息的约束最大似然方法更有效。我们通过使用来自大型宫颈筛查项目的数据评估宫颈癌风险来说明我们方法的应用。