Semiparametric analysis of linear transformation models with covariate measurement errors

• Published in: Biometrics Vol. 70; no. 1; pp. 21 - 32
• Main Authors: Sinha, Samiran, Ma, Yanyuan
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishers 01.03.2014; Blackwell Publishing Ltd; International Biometric Society
• Subjects: Anti-HIV Agents - pharmacology; Bioinformatics; Biomarkers - analysis; BIOMETRIC METHODOLOGY; Biometrics; biometry; CD4 Lymphocyte Count; Censored data; Clinical trials; Computer Simulation; Counting process; data collection; Data Interpretation, Statistical; equations; Estimating equation; Estimation bias; Estimation methods; Estimators; HIV Infections - drug therapy; HIV-1 - immunology; Humans; Induced hazard; Kernel density; Linear Models; Linear transformations; Longitudinal Studies - methods; Measurement errors; Non-differential measurement errors; Parameter estimation; Parametric models; Sampling bias; Standard error; U-statistics; Counting process; Estimating equation; Non-differential measurement errors; Kernel density; Induced hazard; U-statistics
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.12119

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.