A selection model for longitudinal binary responses subject to non-ignorable attrition.
Longitudinal studies collect information on a sample of individuals which is followed over time to analyze the effects of individual and time-dependent characteristics on the observed response. These studies often suffer from attrition: individuals drop out of the study before its completion time and thus present incomplete data records. When the missing mechanism, once conditioned on other (observed) variables, does not depend on current (eventually unobserved) values of the response variable, the dropout mechanism is known to be ignorable. We propose a selection model extending semiparametric variance component models for longitudinal binary responses to allow for dependence between the missing data mechanism and the primary response process. The model is applied to a data set from a methadone maintenance treatment programme held in Sidney, 1986.
Dipartimento di Statistica, Probabilità e Statistiche Applicate, Sapienza Università di Roma, Italy.
Marco
M
Alfò
Antonello
A
Maruotti
D001699:Biometry; D016208:Databases, Factual; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008691:Methadone / Q000627:therapeutic use; D015233:Models, Statistical; D009293:Opioid-Related Disorders / Q000534:rehabilitation; D012044:Regression Analysis; D018709:Statistics, Nonparametric
D008691:Methadone