Asymptotic Bias in the Linear Mixed Effects Model Under Non-Ignorable Missing Data Mechanisms
不可忽略缺失机制下线性混合效应模型的渐近偏差
Summary In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation,…