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科学问题的研究。整合这些研究中的汇总信息以提升内部研究中参数估计的效率，正日益受到关注。本研究针对数据采用半参数模型建模的情形，提出了一个整合外部研究汇总信息的系统性框架。该新型框架提供了能够利用辅助信息更新传统估计的简便估计量，并通过深入挖掘问题内在的复杂数学结构解决了计算难题。我们论证了所提估计量在理论上比仅基于内部数据的初始估计更有效的条件。文中还以生存分析中的比例风险模型等特例为例，提供了数值算例。