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

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.

author list (cited authors)

  • Sinha, S., & Ma, Y.

citation count

  • 7

publication date

  • December 2013

publisher