Wu M C, Bailey K R
Division of Epidemiology, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892.
Biometrics. 1989 Sep;45(3):939-55.
A general linear regression model for the usual least squares estimated rate of change (slope) on censoring time is described as an approximation to account for informative right censoring in estimating and comparing changes of a continuous variable in two groups. Two noniterative estimators for the group slope means, the linear minimum variance unbiased (LMVUB) estimator and the linear minimum mean squared error (LMMSE) estimator, are proposed under this conditional model. In realistic situations, we illustrate that the LMVUB and LMMSE estimators, derived under a simple linear regression model, are quite competitive compared to the pseudo maximum likelihood estimator (PMLE) derived by modeling the censoring probabilities. Generalizations to polynomial response curves and general linear models are also described.
一个用于在删失时间上进行普通最小二乘估计变化率（斜率）的一般线性回归模型被描述为一种近似方法，用于在估计和比较两组中连续变量的变化时考虑信息性右删失。在这个条件模型下，提出了两种用于组斜率均值的非迭代估计器，即线性最小方差无偏（LMVUB）估计器和线性最小均方误差（LMMSE）估计器。在实际情况中，我们表明，在简单线性回归模型下推导的LMVUB和LMMSE估计器，与通过对删失概率建模得到的伪最大似然估计器（PMLE）相比具有相当的竞争力。还描述了对多项式响应曲线和一般线性模型的推广。
