Semiparametric transformation models are considered for failure time data from case-cohort studies, where the covariates are assembled only for a ran-domly selected subcohort from the entire cohort and additional cases outside the subcohort. We present the estimating procedures for the regression parameters and survival probability. The asymptotic properties of the resulting estimators are developed based on asymptotic results for U-statistics, martingales, stochastic processes and finite population sampling
The proportional hazards assumption in the commonly used Cox model for censored failure time data is...
Semicompeting risk outcome data, e.g. time to disease progression and time to death, are commonly co...
Efficiencies of the maximum pseudolikelihood estimator and a number of related estimators for the ca...
Semiparametric transformation models are considered for failure time data from case-cohort studies, ...
A general class of semiparametric transformation models is studied for analysing survival data from ...
In a case–cohort design, covariates are assembled only for a subcohort that is randomly selected fro...
Under two-phase cohort designs, such as case-cohort and nested case-control sampling, information on...
In stratified case-cohort designs, samplings of case-cohort samples are conducted via a stratified r...
Abstract: Case-cohort design, an outcome-dependent sampling design for censored survival data, is in...
A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment ...
The class of semiparametric transformation models provides a very general framework for studying the...
AbstractEfficiencies of the maximum pseudolikelihood estimator and a number of related estimators fo...
Semiparametric transformation model has been extensively investigated in the literature. The model, ...
We propose a class of transformation models for survival data with a cure fraction. The class of tra...
The nonparametric transformation model for survival time that makes no parametric assumptions on bot...
The proportional hazards assumption in the commonly used Cox model for censored failure time data is...
Semicompeting risk outcome data, e.g. time to disease progression and time to death, are commonly co...
Efficiencies of the maximum pseudolikelihood estimator and a number of related estimators for the ca...
Semiparametric transformation models are considered for failure time data from case-cohort studies, ...
A general class of semiparametric transformation models is studied for analysing survival data from ...
In a case–cohort design, covariates are assembled only for a subcohort that is randomly selected fro...
Under two-phase cohort designs, such as case-cohort and nested case-control sampling, information on...
In stratified case-cohort designs, samplings of case-cohort samples are conducted via a stratified r...
Abstract: Case-cohort design, an outcome-dependent sampling design for censored survival data, is in...
A common objective of biomedical cohort studies is assessing the effect of a time-varying treatment ...
The class of semiparametric transformation models provides a very general framework for studying the...
AbstractEfficiencies of the maximum pseudolikelihood estimator and a number of related estimators fo...
Semiparametric transformation model has been extensively investigated in the literature. The model, ...
We propose a class of transformation models for survival data with a cure fraction. The class of tra...
The nonparametric transformation model for survival time that makes no parametric assumptions on bot...
The proportional hazards assumption in the commonly used Cox model for censored failure time data is...
Semicompeting risk outcome data, e.g. time to disease progression and time to death, are commonly co...
Efficiencies of the maximum pseudolikelihood estimator and a number of related estimators for the ca...