With the development of biomedical science, researchers have increasing access to an abundance of studies focusing on similar research questions. There is a growing interest in the integration of summary information from those studies to enhance the efficiency of estimation in their own internal studies. In this work, we present a comprehensive framework on integration of summary information from external studies when the data are modeled by semiparametric models. Our novel framework offers straightforward estimators that update conventional estimations with auxiliary information. It addresses computational challenges by capitalizing on the intricate mathematical structure inherent to the problem. We demonstrate the conditions when the proposed estimators are theoretically more efficient than initial estimate based solely on internal data. Several special cases such as proportional hazards model in survival analysis are provided with numerical examples.

翻译：随着生物医学科学的发展，研究者们越来越多地接触到大量关注相似研究问题的外部研究。整合这些研究的汇总信息以提升自身内部研究的估计效率，已成为一个日益受关注的研究方向。本文针对基于半参数模型建模的数据，提出了一套整合外部研究汇总信息的综合性框架。该创新框架能够通过利用辅助信息，对传统估计方法进行直接更新，得到简洁的估计量。通过挖掘问题内在的复杂数学结构，该框架有效解决了计算难题。我们证明了所提估计量在特定条件下理论上比仅基于内部数据的初始估计更高效。此外，本文还以生存分析中的比例风险模型等若干特例为例，提供了数值验证。