A Class of Weighted Estimating Equations for Semiparametric Transformation Models with Missing Covariates
针对生存分析中协变量缺失的问题，提出一类加权估计方程，适用于右删失数据下的半参数变换模型，并引入新无偏估计方程提升效率，所得估计量具有双重稳健性和最优性。
Abstract In survival analysis, covariate measurements often contain missing observations; ignoring this feature can lead to invalid inference. We propose a class of weighted estimating equations for right‐censored data with missing covariates under semiparametric transformation models. Time‐specific and subject‐specific weights are accommodated in the formulation of the weighted estimating equations. We establish unified results for estimating missingness probabilities that cover both parametric and non‐parametric modelling schemes. To improve estimation efficiency, the weighted estimating equations are augmented by a new set of unbiased estimating equations. The resultant estimator has the so‐called ‘double robustness’ property and is optimal within a class of consistent estimators.