Cheng Hao
National Academy of Innovation Strategy, China Association for Science and Technology, Beijing, China.
School of Statistics, Renmin University of China, Beijing, China.
Pharm Stat. 2021 Jan;20(1):25-38. doi: 10.1002/pst.2052. Epub 2020 Aug 18.
By employing all the observed information and the optimal augmentation term, we propose an augmented inverse probability weighted fractional imputation method (AFI) to handle covariates missing at random in quantile regression. Compared with the existing completely case analysis, inverse probability weighting, multiple imputation and fractional imputation based on quantile regression model with missing covarites, we carry out simulation study to investigate its performance in estimation accuracy and efficiency, computational efficiency and estimation robustness. We also talk about the influence of imputation replicates in our AFI. Finally, we apply our methodology to part of the National Health and Nutrition Examination Survey data.
通过运用所有观测到的信息和最优扩充项，我们提出了一种扩充逆概率加权分数插补方法（AFI），以处理分位数回归中随机缺失的协变量。与现有的完全病例分析、逆概率加权、多重插补以及基于带有缺失协变量的分位数回归模型的分数插补方法相比，我们进行了模拟研究，以考察其在估计准确性和效率、计算效率以及估计稳健性方面的表现。我们还讨论了AFI中插补重复次数的影响。最后，我们将我们的方法应用于部分国家健康与营养检查调查数据。
