论文标题
使用随机EM算法分析响应中缺少值的纵向数据和协变量的分析
Analysis of Longitudinal Data with Missing Values in the Response and Covariates Using the Stochastic EM Algorithm
论文作者
论文摘要
在纵向数据中，对于一组个体，响应变量是随着时间的时间或不同条件下的。在许多情况下，所有预期的测量都不可用，从而导致缺失值。如果从未遵循观察到的测量值，则会导致辍学模式。缺失值可以在响应变量，协变量或两者中。当缺失的概率取决于缺失值并可能取决于观察到的值时，丢失机制被称为非随机。在这种情况下，应在分析中考虑丢失值，以避免任何潜在的偏差。本文的目的是采用多个候选（MI）来处理协变量中使用的缺失值。选择模型用于在存在非随机辍学的情况下对纵向数据进行建模。除了辍学模型的估计外，开发了随机EM算法（SEM）以获得模型参数估计。 SEM算法不提供估计值的标准误差。我们开发了一种蒙特卡洛方法来获得标准误差。提出的方法性能通过仿真研究评估。同样，提出的方法应用于真实数据集。
In longitudinal data a response variable is measured over time, or under different conditions, for a cohort of individuals. In many situations all intended measurements are not available which results in missing values. If the missing value is never followed by an observed measurement, this leads to dropout pattern. The missing values could be in the response variable, the covariates or in both. The missingness mechanism is termed non-random when the probability of missingness depends on the missing value and may be on the observed values. In this case the missing values should be considered in the analysis to avoid any potential bias. The aim of this article is to employ multiple imputations (MI) to handle missing values in covariates using. The selection model is used to model longitudinal data in the presence of non-random dropout. The stochastic EM algorithm (SEM) is developed to obtain the model parameter estimates in addition to the estimates of the dropout model. The SEM algorithm does not provide standard errors of the estimates. We developed a Monte Carlo method to obtain the standard errors. The proposed approach performance is evaluated through a simulation study. Also, the proposed approach is applied to a real data set.
