The paper discusses the regression analysis of current status data,which is common in various fields such as tumorigenic research and demographic studies.Analyzing this type of data poses a significant challenge and h...The paper discusses the regression analysis of current status data,which is common in various fields such as tumorigenic research and demographic studies.Analyzing this type of data poses a significant challenge and has recently gained considerable interest.Furthermore,the authors consider an even more difficult scenario where,apart from censoring,one also faces left-truncation and informative censoring,meaning that there is a potential correlation between the examination time and the failure time of interest.The authors propose a sieve maximum likelihood estimation(MLE)method and in the proposed method for inference,a copula-based procedure is applied to depict the informative censoring.Additionally,the authors utilise the splines to estimate the unknown nonparametric functions in the model,and the asymptotic properties of the proposed estimator are established.The simulation results indicate that the developed approach is effective in practice,and it has been successfully applied to a set of real data.展开更多
This paper discusses variable selection for interval-censored failure time data,a general type of failure time data that commonly arise in many areas such as clinical trials and follow-up studies.Although some methods...This paper discusses variable selection for interval-censored failure time data,a general type of failure time data that commonly arise in many areas such as clinical trials and follow-up studies.Although some methods have been developed in the literature for the problem,most of the existing procedures apply only to specific models.In this paper,we consider the data arising from a general class of partly linear additive generalized odds rate models and propose a penalized variable selection approach through maximizing a derived penalized likelihood function.In the method,the Bernsetin polynomials are employed to approximate both the unknown baseline hazard functions and the nonlinear covariate effects functions,and for the implementation of the method,a coordinate descent algorithm is developed.Also the asymptotic properties of the proposed estimators,including the oracle property,are established.An extensive simulation study is conducted to assess the finite-sample performance of the proposed estimators and indicates that it works well in practice.Finally,the proposed method is applied to a set of real data on Alzheimer’s disease.展开更多
We discuss regression analysis of current status data with the additive hazards model when the failure status may suffer misclassification.Such data occur commonly in many scientific fields involving the diagnosis tes...We discuss regression analysis of current status data with the additive hazards model when the failure status may suffer misclassification.Such data occur commonly in many scientific fields involving the diagnosis test with imperfect sensitivity and specificity.In particular,we consider the situation where the sensitivity and specificity are known and propose a nonparametric maximum likelihood approach.For the implementation of the method,a novel EM algorithm is developed,and the asymptotic properties of the resulting estimators are established.Furthermore,the estimated regression parameters are shown to be semiparametrically efficient.We demonstrate the empirical performance of the proposed methodology in a simulation study and show its substantial advantages over the naive method.Also an application to a motivated study on chlamydia is provided.展开更多
This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estim...This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.展开更多
Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,anoth...Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation times.It is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading results.In this paper,the authors consider such data and propose a frailty-based inference procedure.In particular,an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established.The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.12171328,12001093,12231011,and 12071176the National Key Research and Development Program of China under Grant No.2020YFA0714102Beijing Natural Science Foundation under Grant No.Z210003。
文摘The paper discusses the regression analysis of current status data,which is common in various fields such as tumorigenic research and demographic studies.Analyzing this type of data poses a significant challenge and has recently gained considerable interest.Furthermore,the authors consider an even more difficult scenario where,apart from censoring,one also faces left-truncation and informative censoring,meaning that there is a potential correlation between the examination time and the failure time of interest.The authors propose a sieve maximum likelihood estimation(MLE)method and in the proposed method for inference,a copula-based procedure is applied to depict the informative censoring.Additionally,the authors utilise the splines to estimate the unknown nonparametric functions in the model,and the asymptotic properties of the proposed estimator are established.The simulation results indicate that the developed approach is effective in practice,and it has been successfully applied to a set of real data.
基金Supported by the National Natural Science Foundation of China(Grant Nos.12071176,12031016,12171328)Scientific and Technologial Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2023L012)Beijing Natural Science Foundation(Grant No.Z210003)。
文摘This paper discusses variable selection for interval-censored failure time data,a general type of failure time data that commonly arise in many areas such as clinical trials and follow-up studies.Although some methods have been developed in the literature for the problem,most of the existing procedures apply only to specific models.In this paper,we consider the data arising from a general class of partly linear additive generalized odds rate models and propose a penalized variable selection approach through maximizing a derived penalized likelihood function.In the method,the Bernsetin polynomials are employed to approximate both the unknown baseline hazard functions and the nonlinear covariate effects functions,and for the implementation of the method,a coordinate descent algorithm is developed.Also the asymptotic properties of the proposed estimators,including the oracle property,are established.An extensive simulation study is conducted to assess the finite-sample performance of the proposed estimators and indicates that it works well in practice.Finally,the proposed method is applied to a set of real data on Alzheimer’s disease.
基金Shuwei Li's research was partially supported by the National Nature Science Foundation of China(Grant No.11901128)Nature Science Foundation of Guangdong Province of China(Grant Nos.2021A1515010044,2022A1515011901)+3 种基金Science and Technology Program of Guangzhou of China(Grant No.202102010512)the National Statistical Science Research Project(Grant No.2022LY041)Shishun Zhao's research was partially supported by the National Nature Science Foundation of China(Grant No.12071176)the Science and Technology Developing Plan of Jilin Province(20200201258JC).
文摘We discuss regression analysis of current status data with the additive hazards model when the failure status may suffer misclassification.Such data occur commonly in many scientific fields involving the diagnosis test with imperfect sensitivity and specificity.In particular,we consider the situation where the sensitivity and specificity are known and propose a nonparametric maximum likelihood approach.For the implementation of the method,a novel EM algorithm is developed,and the asymptotic properties of the resulting estimators are established.Furthermore,the estimated regression parameters are shown to be semiparametrically efficient.We demonstrate the empirical performance of the proposed methodology in a simulation study and show its substantial advantages over the naive method.Also an application to a motivated study on chlamydia is provided.
基金supported by the National Natural Science Foundation of China under Grant No.11671168the Science and Technology Developing Plan of Jilin Province under Grant No.20200201258JC。
文摘This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.
基金supported by the National Natural Science Foundation of China under Grant Nos. 12001093,12071176the National Key Research and Development Program of China under Grant No. 2020YFA0714102the Science and Technology Developing Plan of Jilin Province under Grant No. 20200201258JC
文摘Misclassified current status data arises if each study subject can only be observed once and the observation status is determined by a diagnostic test with imperfect sensitivity and specificity.For the situation,another issue that may occur is that the observation time may be correlated with the interested failure time,which is often referred to as informative censoring or observation times.It is well-known that in the presence of informative censoring,the analysis that ignores it could yield biased or even misleading results.In this paper,the authors consider such data and propose a frailty-based inference procedure.In particular,an EM algorithm based on Poisson latent variables is developed and the asymptotic properties of the resulting estimators are established.The numerical results show that the proposed method works well in practice and an application to a set of real data is provided.
