Latent-model robustness in joint models for a primary endpoint and a longitudinal process.
Joint modeling of a primary response and a longitudinal process via shared random effects is widely used in many areas of application. Likelihood-based inference on joint models requires model specification of the random effects. Inappropriate model specification of random effects can compromise inference. We present methods to diagnose random effect model misspecification of the type that leads to biased inference on joint models. The methods are illustrated via application to simulated data, and by application to data from a study of bone mineral density in perimenopausal women and data from an HIV clinical trial.
Huang
Xianzheng
X
Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA. huang@stat.sc.edu
Stefanski
Leonard A
LA
Davidian
Marie
M
D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D023381:Endpoint Determination / Q000379:methods*; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis