Using Multiple Imputation to Address Missing Values of Hierarchical Data

Authors

Yujia Zhang, Centers for Disease Control and Prevention, AtlantaFollow
Sara Crawford, Centers for Disease Control and Prevention, AtlantaFollow
Sheree Boulet, Centers for Disease Control and Prevention, AtlantaFollow
Michael Monsour, Centers for Disease Control and Prevention, AtlantaFollow
Bruce Cohen, Massachusetts Department of Public Health, BostonFollow
Patricia McKane, Michigan Department of Community Health, LansingFollow
Karen Freeman, Florida Department of Health, TallahasseeFollow

Abstract

Missing data may be a concern for data analysis. If it has a hierarchical or nested structure, the SUDAAN package can be used for multiple imputation. This is illustrated with birth certificate data that was linked to the Centers for Disease Control and Prevention’s National Assisted Reproductive Technology Surveillance System database. The Cox-Iannacchione weighted sequential hot deck method was used to conduct multiple imputation for missing/unknown values of covariates in a logistic model.

DOI

10.22237/jmasm/1493599140

Recommended Citation

Zhang, Y., Crawford, S., Boulet, S., Monsour, M., Cohen, B., McKane, P., & Freeman, K. (2017). Using multiple imputation to address missing values of hierarchical data. Journal of Modern Applied Statistical Methods, 16(1), 744-752. doi: 10.22237/jmasm/1493599140