A correlated random-effects model for normal longitudinal data with nonignorable missingness

• Published in: Statistics in medicine Vol. 29; no. 2; pp. 236 - 247
• Main Authors: Lin, Huazhen, Liu, Danping, Zhou, Xiao-Hua
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 30.01.2010; Wiley Subscription Services, Inc
• Subjects: Adolescent; Algorithms; Analysis of Variance; Approximation; Autism; Autistic Disorder - diagnosis; Autistic Disorder - psychology; Bias; Child; Child, Preschool; Cluster analysis; Computer Simulation; correlated random effects; Correlation analysis; Epidemiologic Research Design; Hierarchies; Humans; Language Development; Laplace approximation; Likelihood Functions; longitudinal data; Longitudinal Studies; Models, Statistical; nonignorable missing; Parameter estimation; Probability; Sample Size; Socialization
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.3760

The missing data problem is common in longitudinal or hierarchical structure studies. In this paper, we propose a correlated random‐effects model to fit normal longitudinal or cluster data when the missingness mechanism is nonignorable. Computational challenges arise in the model fitting due to intractable numerical integrations. We obtain the estimates of the parameters based on an accurate approximation of the log likelihood, which has higher‐order accuracy but with less computational burden than the existing approximation. We apply the proposed method it to a real data set arising from an autism study. Copyright © 2009 John Wiley & Sons, Ltd.