A nonparametric multiple imputation approach for data with missing covariate values with application to colorectal adenoma data.
A nearest neighbor-based multiple imputation approach is proposed to recover missing covariate information using the predictive covariates while estimating the association between the outcome and the covariates. To conduct the imputation, two working models are fitted to define an imputing set. This approach is expected to be robust to the underlying distribution of the data. We show in simulation and demonstrate on a colorectal data set that the proposed approach can improve efficiency and reduce bias in a situation with missing at random compared to the complete case analysis and the modified inverse probability weighted method.
a Division of Epidemiology and Biostatistics, College of Public Health , University of Arizona , Tucson , Arizona , USA.
Chiu-Hsieh
CH
Hsu
Qi
Q
Long
Yisheng
Y
Li
Elizabeth
E
Jacobs
D000236:Adenoma / Q000097:blood* / Q000453:epidemiology; D000367:Age Factors; D015415:Biomarkers / Q000097:blood; D015179:Colorectal Neoplasms / Q000097:blood* / Q000453:epidemiology; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D012737:Sex Factors; D018709:Statistics, Nonparametric; D014807:Vitamin D / Q000097:blood*
D015415:Biomarkers; D014807:Vitamin D
Missing at random; Multiple imputation; Nearest neighbor; Nonparametric imputation