Application of pattern-mixture models to outcomes that are potentially missing not at random using pseudo maximum likelihood estimation
Division of Biostatistics, School of Medicine, Indiana University, 1050 Wishard Boulevard RG R4101, Indianapolis, IN 46202, USA
Department of Biostatistics, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261, USA lweis+{at}pitt.edu
* To whom correspondence should be addressed. chashen{at}iupui.edu
In this work, we fit pattern-mixture models to data sets with responses that are potentially missing not at random (MNAR, Little and Rubin, 1987). In estimating the regression parameters that are identifiable, we use the pseudo maximum likelihood method based on exponential families. This procedure provides consistent estimators when the mean structure is correctly specified for each pattern, with further information on the variance structure giving an efficient estimator. The proposed method can be used to handle a variety of continuous and discrete outcomes. A test built on this approach is also developed for model simplification in order to improve efficiency. Simulations are carried out to compare the proposed estimation procedure with other methods. In combination with sensitivity analysis, our approach can be used to fit parsimonious semi-parametric pattern-mixture models to outcomes that are potentially MNAR. We apply the proposed method to an epidemiologic cohort study to examine cognition decline among elderly.
Keywords: Missing data; MNAR; Pattern-mixture model; Pseudo maximum likelihood