Case-cohort design usually requires the disease rate to be low in large cohort study,although it has been extensively used in practice.However,the disease with high rate is frequently observed in many clinical studies...Case-cohort design usually requires the disease rate to be low in large cohort study,although it has been extensively used in practice.However,the disease with high rate is frequently observed in many clinical studies.Under such circumstances,it is desirable to consider a generalized case-cohort design,where only a fraction of cases are sampled.In this article,we propose the inference procedure for the additive hazards regression under the generalized case-cohort sampling.Asymptotic properties of the proposed estimators for the regression coefcients are established.To demonstrate the efectiveness of the generalized case-cohort sampling,we compare it with simple random sampling in terms of asymptotic relative efciency.Furthermore,we derive the optimal allocation of the subsamples for the proposed design.The fnite sample performance of the proposed method is evaluated through simulation studies.展开更多
The accelerated failure time model always offers a valuable complement to the traditional Cox proportional hazards model due to its direct and meaningful interpretation.We propose a variable selection method in the co...The accelerated failure time model always offers a valuable complement to the traditional Cox proportional hazards model due to its direct and meaningful interpretation.We propose a variable selection method in the context of the accelerated failure time model for survival data,which can simultaneously complete variable selection and parameter estimation.Meanwhile,the proposed method can deal with the potential outliers in survival times as well as heteroscedastic model errors,which are frequently encountered in practice.Specifically,utilizing the general nonconvex penalty,we propose the adaptive penalized weighted least absolute deviation estimator for the accelerated failure time model.Under some regularity conditions,we show that the proposed method yields consistent estimator and possesses the oracle property.In addition,we propose a new algorithm to compute the estimate in the high dimensional settings,and evaluate the practical utility of the proposed method through extensive simulation studies and two real examples.展开更多
基金supported by the Fundamental Research Fund for the Central Universitiessupported by National Natural Science Foundation of China(Grant No.11301545)supported by National Natural Science Foundation of China(Grant No.11171263)
文摘Case-cohort design usually requires the disease rate to be low in large cohort study,although it has been extensively used in practice.However,the disease with high rate is frequently observed in many clinical studies.Under such circumstances,it is desirable to consider a generalized case-cohort design,where only a fraction of cases are sampled.In this article,we propose the inference procedure for the additive hazards regression under the generalized case-cohort sampling.Asymptotic properties of the proposed estimators for the regression coefcients are established.To demonstrate the efectiveness of the generalized case-cohort sampling,we compare it with simple random sampling in terms of asymptotic relative efciency.Furthermore,we derive the optimal allocation of the subsamples for the proposed design.The fnite sample performance of the proposed method is evaluated through simulation studies.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11671311,11771250 and 11971324)the Natural Science Foundation of Shandong Province(Grant No.ZR2019MA002)the National Key Research and Development Program of China(Grant No.2018YFC1314600)。
文摘The accelerated failure time model always offers a valuable complement to the traditional Cox proportional hazards model due to its direct and meaningful interpretation.We propose a variable selection method in the context of the accelerated failure time model for survival data,which can simultaneously complete variable selection and parameter estimation.Meanwhile,the proposed method can deal with the potential outliers in survival times as well as heteroscedastic model errors,which are frequently encountered in practice.Specifically,utilizing the general nonconvex penalty,we propose the adaptive penalized weighted least absolute deviation estimator for the accelerated failure time model.Under some regularity conditions,we show that the proposed method yields consistent estimator and possesses the oracle property.In addition,we propose a new algorithm to compute the estimate in the high dimensional settings,and evaluate the practical utility of the proposed method through extensive simulation studies and two real examples.
