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:许多观测研究和临床试验收集了可能与主要终点高度相关的各种次要结果,这些次要结果往往在次要分析中与主要数据分析分开分析,但这些次要结果可用于提高主要分析的估计精确度,我们建议一种称为“多重信息借阅”的方法,从次要数据(包含次要结果和共变)中借取信息,以提高主要分析的效率,提议的方法是针对二级数据模型的错误区分而强劲的,理论和案例研究都表明,在效率收益方面,敏博比现有方法要好。我们应用敏博来使用社区亚瑟罗塞松塞风险研究的数据来评估高血压风险因素。</s>