A Nonparametric Multiple Imputation Approach for Data with Missing Covariate Values with Application to Colorectal Adenoma Data

• Authors: Chiu-Hsieh Hsu, University of Arizona; Qi Long, Emory University; Yisheng Li, University of Texas MD Anderson Cancer Center; Elizabeth Jacobs, University of Arizona
• Journal Title: Journal of Biopharmaceutical Statistics
• Volume: Volume 24, Number 3
• Publisher: Taylor & Francis: STM, Behavioural Science and Public Health Titles | 2014-05-04, Pages 634-648
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1080/10543406.2014.888444
• Subjects: Biology, Biostatistics; Biology, Bioinformatics; Health Sciences, Oncology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Physical Sciences; Pharmacology & Pharmacy; Statistics & Probability; Mathematics; Missing at random; Multiple imputation; Nearest neighbor; Nonparametric imputation; RECURRENCE; LIKELIHOOD; REGRESSION; MODELS; TRIAL

Abstract

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.