Intrinsic efficiency and multiple robustness in longitudinal studies with drop-out: Table 1.
本文针对纵向研究中因退出导致的缺失数据，提出一种估计终点响应均值的方法，通过校准每次访视的缺失概率并利用多个数据分布模型，实现内在效率与多重稳健性。
Intrinsic efficiency and multiple robustness are desirable properties in missing data analysis. We establish both for estimating the mean of a response at the end of a longitudinal study with drop-out. The idea is to calibrate the estimated missingness probability at each visit using data from past visits. We consider one working model for the missingness probability and multiple working models for the data distribution. Intrinsic efficiency guarantees that, when the missingness probability is correctly modelled, the multiple data distribution models, combined with data prior to the end of the study, are optimally accommodated to maximize efficiency. The efficiency generally increases with the number of data distribution models, except where one such model is correctly specified as well, in which case all the proposed estimators attain the semiparametric efficiency bound. Multiple robustness ensures estimation consistency if the missingness probability model is misspecified but one data distribution model is correct. Our proposed estimators are all convex combinations of the observed responses, and thus always fall within the parameter space.