Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies

• Published in: Biometrics Vol. 60; no. 2; pp. 295 - 305
• Main Authors: Lin, Haiqun, McCulloch, Charles E., Rosenheck, Robert A.
• Format: Journal Article
• Language: English
• Published: 350 Main Street , Malden , MA 02148 , U.S.A , and P.O. Box 1354, 9600 Garsington Road , Oxford OX4 2DQ , U.K Blackwell Publishing 01.06.2004; International Biometric Society; Blackwell Publishing Ltd
• Subjects: behavior disorders; Biometrics; Biometry; Community Mental Health Services; Conditional independence assumption; Data analysis; data collection; Data Interpretation, Statistical; dropouts; Homeless people; Homeless Persons - psychology; Homelessness; Humans; Induced substructures; Intermittent missing data; Joint analysis; Latent class; Longitudinal data; Longitudinal Studies; Mental disorders; Mental Disorders - therapy; Mental health services; Missing data; Modeling; Models, Statistical; Parametric models; School dropouts; Simulation; Trajectories; Visit process; Vouchers
• DOI: 10.1111/j.0006-341X.2004.00173.x
• ISSN: 0006-341X

A frequently encountered problem in longitudinal studies is data that are missing due to missed visits or dropouts. In the statistical literature, interest has primarily focused on monotone missing data (dropout) with much less work on intermittent missing data in which a subject may return after one or more missed visits. Intermittent missing data have broader applicability that can include the frequent situation in which subjects do not have common sets of visit times or they visit at nonprescheduled times. In this article, we propose a latent pattern mixture model (LPMM), where the mixture patterns are formed from latent classes that link the longitudinal response and the missingness process. This allows us to handle arbitrary patterns of missing data embodied by subjects' visit process, and avoids the need to specify the mixture patterns a priori. One assumption of our model is that the missingness process is assumed to be conditionally independent of the longitudinal outcomes given the latent classes. We propose a noniterative approach to assess this key assumption. The LPMM is illustrated with a data set from a health service research study in which homeless people with mental illness were randomized to three different service packages and measures of homelessness were recorded at multiple time points. Our model suggests the presence of four latent classes linking subject visit patterns to homeless outcomes.