A bidimensional finite mixture model for longitudinal data subject to dropout

• Published in: Statistics in medicine Vol. 37; no. 20; pp. 2998 - 3011
• Main Authors: Spagnoli, Alessandra, Marino, Maria Francesca, Alfò, Marco
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 10.09.2018
• Subjects: Aged, 80 and over; Algorithms; Cognition Disorders - genetics; Female; Humans; informative missingness; latent variables; Longitudinal Studies; Lost to Follow-Up; Male; Medical research; Mental Status and Dementia Tests; Models, Statistical; Netherlands; nonparametric maximum likelihood; Nonparametric statistics; sensitivity to nonignorability; Netherlands; nonparametric maximum likelihood; sensitivity to nonignorability; latent variables; informative missingness

In longitudinal studies, subjects may be lost to follow up and, thus, present incomplete response sequences. When the mechanism underlying the dropout is nonignorable, we need to account for dependence between the longitudinal and the dropout process. We propose to model such a dependence through di...