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Augmented inverse probability weighted fractional imputation in quantile regression
Author(s) -
Cheng Hao
Publication year - 2020
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.2052
Subject(s) - imputation (statistics) , inverse probability weighting , missing data , statistics , quantile , quantile regression , covariate , mathematics , regression , robustness (evolution) , weighting , national health and nutrition examination survey , inverse probability , computer science , econometrics , regression analysis , estimator , medicine , population , posterior probability , bayesian probability , biochemistry , chemistry , environmental health , radiology , gene
Summary 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.