Logo image
Back
Joint Models for a Primary Endpoint and Multiple Longitudinal Covariate Processes
Journal article   Peer reviewed

Joint Models for a Primary Endpoint and Multiple Longitudinal Covariate Processes

Erning Li, Naisyin Wang and Nae-Yuh Wang
Biometrics, Vol.63(4), pp.1068-1078
05/14/2007
DOI: 10.1111/j.1541-0420.2007.00822.x
PMCID: PMC4443486
PMID: 17501940

View Online

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 et al. (2004 , Biometrics 60 , 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 existing procedure is fairly sensitive to the independent measurement error assumption. To overcome this limitation, we propose new estimation procedures that require neither a distributional or covariance structural assumption on covariate random-effects nor an independence assumption on within-subject measurement errors. These new procedures are more flexible, readily cover scenarios that have multivariate longitudinal covariate processes and can be calculated using available software. Through simulations and an analysis of data from a hypertension study, we evaluate and illustrate the numerical performances of the new estimators.
 Asymptotic bias Conditional and sufficiency score Generalized linear model Measurement error Multivariate longitudinal data Variance components

Details

Metrics

19 Record Views
28 Times Cited - Web of Science
Logo image