Censored Regression in Semiparametric Transformation Models for Survival Data

We consider a class of semi-parametric transformation models, under which an unknown transformation of the survival time is linear related to the covariates with various error distributions, which are parametrically specified with unknown parameters. Estimators of the coefficients of covariates are obtained from linear least squares procedures and the Class-K method with censored observations. We show that the estimators are consistent and asymptotically normal. This transformation model, coupled with proposed inference procedures, provides many alternatives to the Cox models and survival analysis.