An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis.
An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis.
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一种有效的数据集成方案，用于合成来自多个辅助数据集的信息，以进行主要分析的参数推断。
作者：
Chen,Chixiang;Wang,Ming;Chen,Shuo
许多观察性研究和临床试验收集了各种可能与主要终点高度相关的次要结局。这些次要结果通常在次要分析中与主要数据分析分开分析。然而，这些次要结果可以用来提高主分析的估计精度。为了提高主分析的效率，我们提出了一种多重信息借用（multiple information borrowing, MinBo）的方法，即从次要数据（包含次要结果和协变量）中借用信息。该方法对二次数据的模型错配具有较强的鲁棒性。理论和案例研究都表明，民博在效率增益方面优于现有方法。我们将MinBo应用于社区动脉粥样硬化风险研究的数据，以评估高血压的危险因素。
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.