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Bishop, BrendenExamining Random-Coeffcient Pattern-Mixture Models for Longitudinal Data with Informative Dropout
Doctor of Philosophy, The Ohio State University, 2017, Psychology
Missing data commonly arise during longitudinal measurements. Dropout is a particular troublesome type of missingness because inference after the dropout occasion is effectively precluded at the level of the individual without substantial assumptions. If missingness, such as dropout, is related to the unobserved outcome variables, then parameter estimates derived from models which ignore the missingness will be biased. For example, a treatment effect may appear less substantial if poor-performing subjects tend to withdraw from the study. In a general sense, missing data lead to scenarios in which the empirical distribution of observed data is lacking nominal coverage in some areas. Little (1993) proposed a general pattern-mixture model approach in which the moments of the full data distribution were estimated as a finite mixture across the various missing-data patterns. These models and their extensions are flexible and may be estimated using wildly available mixed-modeling software in some special cases. The purpose of this work is to review the relevant missing-data literature and to examine the viability of random-coeffcient pattern-mixture models as an option for analysts seeking unbiased inference for longitudinal data subject to pernicious dropout.


Robert Cudeck (Advisor); DeBoeck Paulus (Committee Member); MacEachern Steve (Committee Member)




Pattern-Mixture Model;Longitudinal;Dropout;Missing Data;NMAR;Nonignorable Missingness