A correlated random-effects model for normal longitudinal data with nonignorable missingness.
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.
Lin
Huazhen
H
School of Mathematics, Sichuan University, Chengdu, Sichuan 610064, People's Republic of China.
Liu
Danping
D
Zhou
Xiao-Hua
XH
D000293:Adolescent; D000465:Algorithms; D000704:Analysis of Variance; D001321:Autistic Disorder / Q000175:diagnosis / Q000523:psychology; D015982:Bias; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D015340:Epidemiologic Research Design; D006801:Humans; D007804:Language Development; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D011336:Probability; D018401:Sample Size; D012951:Socialization