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

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 on...