Parametric latent class joint model for a longitudinal biomarker and recurrent events.
A joint model for a longitudinal biomarker and recurrent events is proposed. This general model accommodates the effects of covariates on the biomarker and event processes, the effects of accumulating event occurrences, and effects caused by interventions after each event occurrence. Association between the biomarker and recurrent event processes is captured through a latent class structure, which also serves to handle an underlying heterogeneous population. We use the EM algorithm for maximum likelihood estimation of the model parameters and a penalized likelihood measure to determine the number of latent classes. This joint model is validated by simulation and illustrated with a data set from epileptic seizure study.
Han
Jun
J
Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA. jhan@mathstat.gsu.edu
Slate
Elizabeth H
EH
Peña
Edsel A
EA
D000465:Algorithms; D015415:Biomarkers; D001699:Biometry; D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004827:Epilepsy / Q000453:epidemiology / Q000503:physiopathology / Q000628:therapy; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008954:Models, Biological; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012008:Recurrence; D016019:Survival Analysis; D016896:Treatment Outcome
D015415:Biomarkers