Recurrent event data are commonly encountered in many scientific fields,including biomedical studies,clinical trials and epidemiological surveys,and many statistical methods have been proposed for their analysis.In th...Recurrent event data are commonly encountered in many scientific fields,including biomedical studies,clinical trials and epidemiological surveys,and many statistical methods have been proposed for their analysis.In this paper,we consider to use a weighted composite endpoint of recurrent and terminal events,which is weighted by the severity of each event,to assess the overall effects of covariates on the two types of events.A flexible additive-multiplicative model incorporating both multiplicative and additive effects on the rate function is proposed to analyze such weighted composite event process,and more importantly,the dependence structure among the recurrent and terminal events is left unspecified.For the estimation,we construct the unbiased estimating equations by virtue of the inverse probability weighting technique,and the resulting estimators are shown to be consistent and asymptotically normal under some mild regularity conditions.We evaluate the finite-sample performance of the proposed method via simulation studies and apply the proposed method to a set of real data arising from a bladder cancer study.展开更多
Recurrent events data with a terminal event (e.g. death) often arise in clinical and observational studies. Most of existing models assume multiplicative covariate effects and model the conditional recurrent event r...Recurrent events data with a terminal event (e.g. death) often arise in clinical and observational studies. Most of existing models assume multiplicative covariate effects and model the conditional recurrent event rate given survival. In this article, we propose a general mSditive-multiplicative rates model for recurrent event data in the presence of a terminal event, where the terminal event stop the further occurrence of recurrent events. Based on the estimating equation approach and the inverse probability weighting technique, we propose two procedures for estimating the regression parameters and the baseline mean function. The asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite-sample behavior of the proposed methods is examined through simulation studies, and an application to a bladder cancer study is also illustrated.展开更多
In this article, we study a class of Box-Cox transformation models for recurrent event data in the presence of terminal event, which includes the proportional means models as special cases. Estimating equation approac...In this article, we study a class of Box-Cox transformation models for recurrent event data in the presence of terminal event, which includes the proportional means models as special cases. Estimating equation approaches and the inverse probability weighting technique are used for estimation of the regression parameters. The asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed methods is examined through simulation studies, and an application to a heart failure study is presented to illustrate the proposed method.展开更多
Longitudinal data often arise when subjects are followed over a period of time, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-...Longitudinal data often arise when subjects are followed over a period of time, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. In this article, we propose joint modeling and analysis of longitudinal data with possibly informative observation times and a dependent terminal event in which a common subject-specific latent variable is used to characterize the correlations. A borrow-strength estimation procedure is developed for parameter estimation, and both large-sample and finite^sample properties of the proposed estimators are established. In addition, some goodness-of-fit methods for assessing the adequacy of the model are provided. An application to a bladder cancer study is illustrated.展开更多
Recurrent event data frequently occur in many longitudinal studies, and the observation on recurrent events could be stopped by a terminal event such as death. This paper considers joint modeling and analysis of recur...Recurrent event data frequently occur in many longitudinal studies, and the observation on recurrent events could be stopped by a terminal event such as death. This paper considers joint modeling and analysis of recurrent event and terminal event data through a common subject-specific frailty, in which the proportional intensity model is used for modeling the recurrent event process and the additive hazards model is used for modeling the terminal event time. Estimating equation approaches are developed for parameter estimation and asymptotic properties of the resulting estimators are established. In addition, some procedures are presented for model checking. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a heart failure study is provided.展开更多
Longitudinal data often occur in follow-up studies, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. We propose a semiparamet...Longitudinal data often occur in follow-up studies, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. We propose a semiparametric mixed effect model with time-varying latent effects in the analysis of longitudinal data with informative observation times and a dependent terminal event. Estimating equation approaches are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a bladder cancer study is provided.展开更多
基金the National Natural Science Foundation of China(Grant Nos.11771431,11690015,11926341,11731015,11901128 and 11601097)Key Laboratory of RCSDS,CAS(Grant No.2008DP173182)+2 种基金Natural Science Foundation of Guangdong Province of China(Grant Nos.2018A030310068,2021A1515010044)University Innovation Team Project of Guangdong Province(Grant No.2020WCXTD018)Science and Technology Program of Guangzhou,China(Grant Nos.202102020368,202102010512)。
文摘Recurrent event data are commonly encountered in many scientific fields,including biomedical studies,clinical trials and epidemiological surveys,and many statistical methods have been proposed for their analysis.In this paper,we consider to use a weighted composite endpoint of recurrent and terminal events,which is weighted by the severity of each event,to assess the overall effects of covariates on the two types of events.A flexible additive-multiplicative model incorporating both multiplicative and additive effects on the rate function is proposed to analyze such weighted composite event process,and more importantly,the dependence structure among the recurrent and terminal events is left unspecified.For the estimation,we construct the unbiased estimating equations by virtue of the inverse probability weighting technique,and the resulting estimators are shown to be consistent and asymptotically normal under some mild regularity conditions.We evaluate the finite-sample performance of the proposed method via simulation studies and apply the proposed method to a set of real data arising from a bladder cancer study.
基金Supported by the National Natural Science Foundation of China(No.11371354)Young Elite Program of Beijing(YETP150)Science and Technology Project of Beijing Municipal Education Commission(KM201411232019)
文摘Recurrent events data with a terminal event (e.g. death) often arise in clinical and observational studies. Most of existing models assume multiplicative covariate effects and model the conditional recurrent event rate given survival. In this article, we propose a general mSditive-multiplicative rates model for recurrent event data in the presence of a terminal event, where the terminal event stop the further occurrence of recurrent events. Based on the estimating equation approach and the inverse probability weighting technique, we propose two procedures for estimating the regression parameters and the baseline mean function. The asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite-sample behavior of the proposed methods is examined through simulation studies, and an application to a bladder cancer study is also illustrated.
基金Supported by National Natural Sciencc Foundation of China(Grant Nos.11301355,11671275,11231010 and11690015)Key Laboratory of RCSDS,CAS(Grant No.2008DP173182),BCMIIS
文摘In this article, we study a class of Box-Cox transformation models for recurrent event data in the presence of terminal event, which includes the proportional means models as special cases. Estimating equation approaches and the inverse probability weighting technique are used for estimation of the regression parameters. The asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed methods is examined through simulation studies, and an application to a heart failure study is presented to illustrate the proposed method.
基金Supported by the National Natural Science Foundation of China Grants(No.11231010 and 11171330)Key Laboratory of RCSDS,CAS(No.2008DP173182)
文摘Longitudinal data often arise when subjects are followed over a period of time, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. In this article, we propose joint modeling and analysis of longitudinal data with possibly informative observation times and a dependent terminal event in which a common subject-specific latent variable is used to characterize the correlations. A borrow-strength estimation procedure is developed for parameter estimation, and both large-sample and finite^sample properties of the proposed estimators are established. In addition, some goodness-of-fit methods for assessing the adequacy of the model are provided. An application to a bladder cancer study is illustrated.
基金supported by the National Natural Science Foundation of China under Grant No.11601080"the Fundamental Research Funds for the Central Universities"in UIBE under Grant No.15QD16supported by the National Natural Science Foundation of China under Grant No.11361015
文摘Recurrent event data frequently occur in many longitudinal studies, and the observation on recurrent events could be stopped by a terminal event such as death. This paper considers joint modeling and analysis of recurrent event and terminal event data through a common subject-specific frailty, in which the proportional intensity model is used for modeling the recurrent event process and the additive hazards model is used for modeling the terminal event time. Estimating equation approaches are developed for parameter estimation and asymptotic properties of the resulting estimators are established. In addition, some procedures are presented for model checking. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a heart failure study is provided.
基金supported by National Natural Science Foundation of China (Grant Nos. 11231010, 11171330 and 11201315)Key Laboratory of Random Complex Structures and Data Science, Chinese Academy of Sciences (Grant No. 2008DP173182)Beijing Center for Mathematics and Information Interdisciplinary Sciences
文摘Longitudinal data often occur in follow-up studies, and in many situations, there may exist informative observation times and a dependent terminal event such as death that stops the follow-up. We propose a semiparametric mixed effect model with time-varying latent effects in the analysis of longitudinal data with informative observation times and a dependent terminal event. Estimating equation approaches are developed for parameter estimation, and asymptotic properties of the resulting estimators are established. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a bladder cancer study is provided.