Many observational studies and clinical trials collect various secondary outcomes that may be highly correlated with the primary endpoint. These secondary outcomes are often analyzed in secondary analyses separately from the main data analysis. However, these secondary outcomes can be used to improve the estimation precision in the main analysis. We propose a method called Multiple Information Borrowing (MinBo) that borrows information from secondary data (containing secondary outcomes and covariates) to improve the efficiency of the main analysis. The proposed method is robust against model misspecification of the secondary data. Both theoretical and case studies demonstrate that MinBo outperforms existing methods in terms of efficiency gain. We apply MinBo to data from the Atherosclerosis Risk in Communities study to assess risk factors for hypertension.

翻译：许多观察性研究和临床试验会收集多种次要结局指标，这些指标可能与主要终点高度相关。在次要分析中，这些次要结局通常独立于主数据分析进行。然而，这些次要结局可被用于提升主分析的估计精度。我们提出一种名为多重信息借用（MinBo）的方法，该方法通过借用次要数据（包含次要结局及协变量）中的信息来提高主分析的效率。所提方法对次要数据的模型误设具有稳健性。理论分析与案例研究均表明，MinBo在效率提升方面优于现有方法。我们将MinBo应用于社区动脉粥样硬化风险研究的数据，以评估高血压的风险因素。