A unified estimation procedure is proposed for the analysis of censored data using linear transformation models, which include the proportional hazards model and the proportional odds model as special cases. This procedure is easily implemented numerically and its validity does not rely on the assumption of independence between the covariates and the censoring variable. The estimator is the same as the Cox partial likelihood estimator in the case of the proportional hazards model. Moreover, the asymptotic variance of the proposed estimator has a closed form and its variance estimator is easily obtained by plug-in rules. The method is illustrated by simulation and is applied to the Veterans' Administration lung cancer data
Recent advances in the transformation model have made it possible to use this model for analyzing a ...
In this work we study the effect of several covariates X on a censored response variable T with unkn...
In this article we study the method of nonparametric regression based on a transformation model, und...
One major aspect in medical research is to relate the survival times of patients with the relevant c...
We propose a semiparametric approach to the proportional hazards regression analysis of interval-cen...
Many widely used models, including proportional hazards models with unobserved heterogeneity, can be...
Semiparametric linear transformation models form a versatile class of regression models with the Cox...
Semiparametric linear transformation models form a versatile class of regression models with the Cox...
In many observational cohort studies, a pair of correlated event times are usually observed for each...
The Cox proportional hazards model and the proportional odds model are some of the popular survival ...
We introduce a flexible family of semiparametric generalized logit-based regression models for survi...
AbstractDoubly censored data, which include left as well as right censored observations, are frequen...
We consider the problem of inference on the regression coefficient in the Cox's proportional hazards...
[[abstract]]In this article we study a semiparametric mixture model for the two-sample problem with ...
We propose a method for fitting semiparametric models such as the proportional hazards (PH), additiv...
Recent advances in the transformation model have made it possible to use this model for analyzing a ...
In this work we study the effect of several covariates X on a censored response variable T with unkn...
In this article we study the method of nonparametric regression based on a transformation model, und...
One major aspect in medical research is to relate the survival times of patients with the relevant c...
We propose a semiparametric approach to the proportional hazards regression analysis of interval-cen...
Many widely used models, including proportional hazards models with unobserved heterogeneity, can be...
Semiparametric linear transformation models form a versatile class of regression models with the Cox...
Semiparametric linear transformation models form a versatile class of regression models with the Cox...
In many observational cohort studies, a pair of correlated event times are usually observed for each...
The Cox proportional hazards model and the proportional odds model are some of the popular survival ...
We introduce a flexible family of semiparametric generalized logit-based regression models for survi...
AbstractDoubly censored data, which include left as well as right censored observations, are frequen...
We consider the problem of inference on the regression coefficient in the Cox's proportional hazards...
[[abstract]]In this article we study a semiparametric mixture model for the two-sample problem with ...
We propose a method for fitting semiparametric models such as the proportional hazards (PH), additiv...
Recent advances in the transformation model have made it possible to use this model for analyzing a ...
In this work we study the effect of several covariates X on a censored response variable T with unkn...
In this article we study the method of nonparametric regression based on a transformation model, und...