Statistics and Its Interface

Volume 6 (2013)

Number 3

A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models

Pages: 315 – 324

DOI: http://dx.doi.org/10.4310/SII.2013.v6.n3.a2

Authors

Qingxia Chen (Department of Biostatistics, Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, U.S.A.)

Joseph G. Ibrahim (Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, N.C., U.S.A.)

Abstract

Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used model-based approaches in missing data problems. Although it is easy to show that when the responses are missing at random (MAR), the complete case analysis is unbiased and efficient, the aforementioned methods are still commonly used in practice for this setting. To examine the performance of and relationships between these three methods in this setting, we derive and investigate small sample and asymptotic expressions of the estimates and standard errors, and fully examine how these estimates are related for the three approaches in the linear regression model when the responses are MAR. We show that when the responses are MAR in the linear model, the estimates of the regression coefficients using these three methods are asymptotically equivalent to the complete case estimates under general conditions. One simulation and a real data set from a liver cancer clinical trial are given to compare the properties of these methods when the responses are MAR.

Keywords

missing data, multiple imputation, maximum likelihood, fully bayesian, missing response, missing at random

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