论文标题
用可用的完整案例丢失值假设处理非单极管缺失数据
Handling Nonmonotone Missing Data with Available Complete-Case Missing Value Assumption
论文作者
论文摘要
在科学研究中，非单独的数据是一个普遍的问题。对于非单身酮丢失数据，传统的无视性和随机丢失（MAR）条件不太可能存在，并且数据分析对于很少的完整数据可能非常具有挑战性。在本文中，我们介绍了用于处理非单身酮和丢失 - 随机（MNAR）问题的可用完整缺失值（ACCMV）假设。我们的ACCMV假设适用于具有一小部分完整观察结果的数据集，我们表明ACCMV假设导致对感兴趣变量的分布的非参数识别。我们进一步提出了一个反概率加权估计器，回归调整估计器和用于估计感兴趣参数的多重稳定估计器。我们研究了所提出的估计量的基本渐近和效率理论。我们通过模拟研究来显示我们方法的有效性，并通过将其应用于电子健康记录中的糖尿病数据集，进一步说明了我们方法的适用性。
Nonmonotone missing data is a common problem in scientific studies. The conventional ignorability and missing-at-random (MAR) conditions are unlikely to hold for nonmonotone missing data and data analysis can be very challenging with few complete data. In this paper, we introduce the available complete-case missing value (ACCMV) assumption for handling nonmonotone and missing-not-at-random (MNAR) problems. Our ACCMV assumption is applicable to data set with a small set of complete observations and we show that the ACCMV assumption leads to nonparametric identification of the distribution for the variables of interest. We further propose an inverse probability weighting estimator, a regression adjustment estimator, and a multiply-robust estimator for estimating a parameter of interest. We studied the underlying asymptotic and efficiency theories of the proposed estimators. We show the validity of our method with simulation studies and further illustrate the applicability of our method by applying it to a diabetes data set from electronic health records.
