title: Semiparametric analysis of linear transformation models with covariate measurement errors.
abstract: We take a semiparametric approach in fitting a linear transformation model to a right censored data when predictive variables are subject to measurement errors. We construct consistent estimating equations when repeated measurements of a surrogate of the unobserved true predictor are available. The proposed approach applies under minimal assumptions on the distributions of the true covariate or the measurement errors. We derive the asymptotic properties of the estimator and illustrate the characteristics of the estimator in finite sample performance via simulation studies. We apply the method to analyze an AIDS clinical trial data set that motivated the work.
authors.affiliation: Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A.
authors.forename: Samiran
authors.initials: S
authors.lastname: Sinha
authors.forename: Yanyuan
authors.initials: Y
authors.lastname: Ma
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology; D015415:Biomarkers / Q000032:analysis*; D018791:CD4 Lymphocyte Count; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015658:HIV Infections / Q000188:drug therapy; D015497:HIV-1 / Q000276:immunology; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies / Q000379:methods*
chemical_list: D019380:Anti-HIV Agents; D015415:Biomarkers
keywords: Counting process; Estimating equation; Induced hazard; Kernel density; Non-differential measurement errors; U-statistics