It is often challenging to share detailed individual-level data among studies due to various informatics and privacy constraints. However, it is relatively easy to pool together aggregated summary level data, such as the ones required for standard meta-analyses. Focusing on data generated from case-control studies, we present a flexible inference procedure that integrates individual-level data collected from an “internal” study with summary data borrowed from “external” studies. This procedure is built on a retrospective empirical likelihood framework to account for the sampling bias in case-control studies. It can incorporate summary statistics extracted from various working models adopted by multiple independent or overlapping external studies. It also allows for external studies to be conducted in a population that is different from the internal study population. We show both theoretically and numerically its efficiency advantage over several competing alternatives.

中文翻译：

---

多项病例对照研究的综合分析

由于各种信息学和隐私限制，在研究之间共享详细的个人层面数据通常具有挑战性。然而，将聚合的汇总级数据汇集在一起​​相对容易，例如标准荟萃分析所需的数据。我们着眼于病例对照研究产生的数据，提出了一种灵活的推理程序，该程序将从“内部”研究中收集的个体水平数据与从“外部”研究中借用的汇总数据相结合。该程序建立在回顾性经验似然框架之上，以解释病例对照研究中的抽样偏差。它可以合并从多个独立或重叠的外部研究采用的各种工作模型中提取的汇总统计数据。它还允许在不同于内部研究人群的人群中进行外部研究。我们在理论上和数值上都展示了它相对于几个竞争替代方案的效率优势。