ingest cdrApp 2018-03-15T15:05:52.666Z d591f2cd-3da7-4b31-9dd8-ee27dcb6a3ee modifyDatastreamByValue RELS-EXT fedoraAdmin 2018-03-15T15:06:42.613Z Setting exclusive relation addDatastream MD_TECHNICAL fedoraAdmin 2018-03-15T15:06:53.793Z Adding technical metadata derived by FITS addDatastream MD_FULL_TEXT fedoraAdmin 2018-03-15T15:07:17.908Z Adding full text metadata extracted by Apache Tika modifyDatastreamByValue RELS-EXT fedoraAdmin 2018-03-15T15:07:39.944Z Setting exclusive relation modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-05-18T14:35:31.827Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-11T10:27:34.091Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-18T06:28:18.849Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-16T19:35:44.402Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-27T15:26:19.356Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-10-12T06:33:55.097Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2019-03-21T16:27:46.150Z Fei Gao Author Department of Biostatistics Gillings School of Global Public Health Semiparametric Regression Analysis of Right- and Interval-Censored Data Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. Summer 2017 2017 Biostatistics Buckley-James estimator, Joint models, Nonparametric likelihood, Random effects, Semiparametric efficiency, Terminal event eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Biostatistics Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text Fei Gao Creator Department of Biostatistics Gillings School of Global Public Health Semiparametric Regression Analysis of Right- and Interval-Censored Data Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. 2017-08 2017 Biostatistics Buckley-James estimator, Joint models, Nonparametric likelihood, Random effects, Semiparametric efficiency, Terminal event eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Biostatistics Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text Fei Gao Creator Department of Biostatistics Gillings School of Global Public Health Semiparametric Regression Analysis of Right- and Interval-Censored Data Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. 2017-08 2017 Biostatistics Buckley-James estimator, Joint models, Nonparametric likelihood, Random effects, Semiparametric efficiency, Terminal event eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Biostatistics Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text Fei Gao Creator Department of Biostatistics Gillings School of Global Public Health Semiparametric Regression Analysis of Right- and Interval-Censored Data Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. 2017-08 2017 Biostatistics Buckley-James estimator, Joint models, Nonparametric likelihood, Random effects, Semiparametric efficiency, Terminal event eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Biostatistics Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text Fei Gao Creator Department of Biostatistics Gillings School of Global Public Health Semiparametric Regression Analysis of Right- and Interval-Censored Data Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. 2017-08 2017 Biostatistics Buckley-James estimator, Joint models, Nonparametric likelihood, Random effects, Semiparametric efficiency, Terminal event eng Doctor of Philosophy Dissertation Biostatistics Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text University of North Carolina at Chapel Hill Degree granting institution Fei Gao Creator Department of Biostatistics Gillings School of Global Public Health Semiparametric Regression Analysis of Right- and Interval-Censored Data Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. 2017-08 2017 Biostatistics Buckley-James estimator; Joint models; Nonparametric likelihood; Random effects; Semiparametric efficiency; Terminal event eng Doctor of Philosophy Dissertation Biostatistics Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text University of North Carolina at Chapel Hill Degree granting institution Fei Gao Creator Department of Biostatistics Gillings School of Global Public Health Semiparametric Regression Analysis of Right- and Interval-Censored Data Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. 2017-08 2017 Biostatistics Buckley-James estimator, Joint models, Nonparametric likelihood, Random effects, Semiparametric efficiency, Terminal event eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Biostatistics Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text Fei Gao Creator Department of Biostatistics Gillings School of Global Public Health Right-censored data arise when the event time can only be observed up to the end of the follow-up, while interval-censored data arise when the event time is only known to lie within an interval. There is a large body of statistical literature on right-censored and interval-censored data, but the existing methods cannot properly handle certain complexities. In the first project, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with partly interval-censored (PIC) data, which arise when the event time may be right-censored for some subjects and interval-censored for the others because of different observation schemes. We generalize the Buckley-James estimator to PIC data and develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We then establish the asymptotic properties of the estimators, conduct extensive simulation studies, and apply our methods to data derived from an AIDS study. In the second project, we consider the setting when subjects may not complete the examination schedule for reasons related to the event of interest. To make a valid inference about the interval-censored event time of interest, we jointly model the event time of interest and the dropout time using transformation models with a shared random effect. We consider nonparametric maximum likelihood estimation (NPMLE) and develop a simple and stable Expectation-maximization (EM) algorithm. We then prove the asymptotic properties of the resulting estimators and show how to predict the event time of interest when dropout is an unavoidable terminal event. Finally, we provide an application to data on the incidence of diabetes from the Atherosclerosis Risk in Communities (ARIC) study. In the third project, we formulate the effects of covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects. We consider NPMLE, develop an EM algorithm, and establish the asymptotic properties of the resulting estimators. We leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history and provide an application to the ARIC study. 2017-08 2017 Biostatistics Buckley-James estimator; Joint models; Nonparametric likelihood; Random effects; Semiparametric efficiency; Terminal event eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Donglin Zeng Thesis advisor Danyu Lin Thesis advisor David Couper Thesis advisor Gerardo Heiss Thesis advisor Michael Hudgens Thesis advisor text Gao_unc_0153D_17350.pdf uuid:45de8c84-4fd8-489d-8202-9179d8bfee29 2019-12-31T00:00:00 2017-08-29T19:28:08Z proquest application/pdf 2813219