Statistics and Its Interface

Volume 5 (2012)

Number 4

Robust inference for longitudinal data analysis with non-ignorable and non-monotonic missing values

Pages: 479 – 490

DOI: http://dx.doi.org/10.4310/SII.2012.v5.n4.a11

Authors

Robert Elashoff (Department of Biomathematics and Biostatistics, School of Medicine and Public Health, University of California at Los Angeles)

Gang Li (Department of Biostatistics, School of Public Health, University of California at Los Angeles)

Ning Li (Biostatistics Core, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, Calif., U.S.A.)

Chi-Hong Tseng (Department of Medicine, School of Medicine, University of California at Los Angeles)

Abstract

A common problem in the longitudinal data analysis is the missing values due to subject’s missed visits and loss to follow up. Although many novel statistical approaches have been developed to handle such data structures in recent years, few methods are available to provide robust inference in the presence of outlying observations. In this paper we propose two methods, $t$-distribution model and robust normal model, for robust inference with non-ignorable nonmonotonic missing data problems in longitudinal studies. These methods are conceptually simple and computationally straight forward. We also conduct simulation studies and use a real data example to demonstrate the performance of these methods.

Keywords

pseudo-likelihood, t-distribution, Huber function, missing not at random

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