title: Estimation in semiparametric transition measurement error models for longitudinal data.
abstract: We consider semiparametric transition measurement error models for longitudinal data, where one of the covariates is measured with error in transition models, and no distributional assumption is made for the underlying unobserved covariate. An estimating equation approach based on the pseudo conditional score method is proposed. We show the resulting estimators of the regression coefficients are consistent and asymptotically normal. We also discuss the issue of efficiency loss. Simulation studies are conducted to examine the finite-sample performance of our estimators. The longitudinal AIDS Costs and Services Utilization Survey data are analyzed for illustration.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, USA. wendy.pan@duke.edu
authors.forename: Wenqin
authors.initials: W
authors.lastname: Pan
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.forename: Xihong
authors.initials: X
authors.lastname: Lin
mesh_terms: D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D008137:Longitudinal Studies; D015233:Models, Statistical; D012044:Regression Analysis; D012106:Research