Information integration plays a pivotal role in biomedical studies by facilitating the combination and analysis of independent datasets from multiple studies, thereby uncovering valuable insights that might otherwise remain obscured due to the limited sample size in individual studies. However, sharing raw data from independent studies presents significant challenges, primarily due to the need to safeguard sensitive participant information and the cumbersome paperwork involved in data sharing. In this article, we first provide a selective review of recent methodological developments in information integration via empirical likelihood, wherein only summary information is required, rather than the raw data. Following this, we introduce a new insight and a potentially promising framework that could broaden the application of information integration across a wider spectrum. Furthermore, this new framework offers computational convenience compared to classic empirical likelihood-based methods. We provide numerical evaluations to assess its performance and discuss various extensions in the end.

翻译：信息整合在生物医学研究中发挥着关键作用，它通过整合与分析来自多项研究的独立数据集，揭示出因单个研究样本量有限而可能被掩盖的重要发现。然而，共享来自独立研究的原始数据面临重大挑战，主要源于保护参与者敏感信息的必要性以及数据共享过程中繁琐的文书工作。本文首先对基于经验似然的信息整合方法的最新进展进行了选择性综述，该方法仅需汇总信息而无需原始数据。随后，我们提出了一种新的见解和一个具有潜力的框架，有望将信息整合的应用拓展至更广泛的领域。此外，与传统的基于经验似然的方法相比，这一新框架提供了计算上的便利性。我们通过数值模拟评估了其性能，并在文末讨论了多种扩展方向。