Semiparametric analysis of linear transformation models with covariate measurement errors. Academic Article

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

• Sinha, Samiran

published proceedings

• Biometrics

author list (cited authors)

• Sinha, S., & Ma, Y.

citation count

• 9

complete list of authors

• Sinha, Samiran||Ma, Yanyuan

publication date

• March 2014

publisher

• Wiley  Publisher

published in

• Biometrics  Journal

keywords

• Anti-HIV Agents
• Biomarkers
• CD4 Lymphocyte Count
• Computer Simulation
• Counting Process
• Data Interpretation, Statistical
• Estimating Equation
• HIV Infections
• HIV-1
• Humans
• Induced Hazard
• Kernel Density
• Linear Models
• Longitudinal Studies
• Non-differential Measurement Errors
• U-statistics

PubMed Central ID

• 24350758

Digital Object Identifier (DOI)

• 10.1111/biom.12119

start page

• 21

end page

• 32

volume

• 70

issue

• 1

URL

• http://dx.doi.org/10.1111/biom.12119