Joint models for a primary endpoint and multiple longitudinal covariate processes.

ABSTRACT: Joint models are formulated to investigate the association between a primary endpoint and features of multiple longitudinal processes. In particular, the subject-specific random effects in a multivariate linear random-effects model for multiple longitudinal processes are predictors in a generalized linear model for primary endpoints. Li, Zhang, and Davidian (2004, Biometrics60, 1-7) proposed an estimation procedure that makes no distributional assumption on the random effects but assumes independent within-subject measurement errors in the longitudinal covariate process. Based on an asymptotic bias analysis, we found that their estimators can be biased when random effects do not fully explain the within-subject correlations among longitudinal covariate measurements. Specifically, the exist

SUBMITTER: Li E 

PROVIDER: S-EPMC4443486 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature