标题： 通过经验似然推进信息集成：选择性综述与新思路 显示英文标题

标题： Advancing Information Integration through Empirical Likelihood: Selective Reviews and a New Idea

摘要： 信息整合在生物医学研究中起着至关重要的作用，通过促进多个独立研究的数据组合与分析，从而揭示出因单个研究样本量有限而可能被掩盖的宝贵见解。 然而，共享独立研究的原始数据面临着重大挑战，主要原因是需要保护敏感的参与者信息，并且涉及繁杂的数据共享手续。 本文首先回顾了近期通过经验似然方法进行信息整合的一些方法学发展，其中仅需汇总信息，而不需要原始数据。 随后，我们介绍了一种新的见解和一个潜在有前景的框架，这可能扩大信息整合在更广泛领域的应用。 此外，与经典的基于经验似然的方法相比，这个新框架提供了计算上的便利性。 我们通过数值评估来评估其性能，并在最后讨论了各种扩展。 显示英文摘要

摘要： 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.