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Author: Xin He
Publisher:
ISBN:
Category : Bladder
Languages : en
Pages :
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Book Description
Panel count data often arise in long term studies that concern occurrence rates of certain recurrent events. In such studies, each subject is observed only at finite discrete time points instead of continuously, and only the number of events that occurred between observation times is known. By multivariate panel count data, we mean that more than one type of recurrent events are of interest. Fields that produce such data include epidemiological studies, medical follow-up studies, reliability studies, and tumorigenicity experiments. This dissertation studies three research problems related to regression analysis of univariate and multivariate panel count data. Semi-parametric regression models and estimation procedures are presented for the situations where observation times or both observation and follow-up times are related with the recurrent events of interest. Their performances are evaluated through simulation studies for practical situations. In addition, the proposed methods are illustrated by application to two data sets from bladder tumor and psoriatic arthritis studies.
Author: Xin He
Publisher:
ISBN:
Category : Bladder
Languages : en
Pages :
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Book Description
Panel count data often arise in long term studies that concern occurrence rates of certain recurrent events. In such studies, each subject is observed only at finite discrete time points instead of continuously, and only the number of events that occurred between observation times is known. By multivariate panel count data, we mean that more than one type of recurrent events are of interest. Fields that produce such data include epidemiological studies, medical follow-up studies, reliability studies, and tumorigenicity experiments. This dissertation studies three research problems related to regression analysis of univariate and multivariate panel count data. Semi-parametric regression models and estimation procedures are presented for the situations where observation times or both observation and follow-up times are related with the recurrent events of interest. Their performances are evaluated through simulation studies for practical situations. In addition, the proposed methods are illustrated by application to two data sets from bladder tumor and psoriatic arthritis studies.
Author: Ni Li
Publisher:
ISBN:
Category : Electronic Dissertations
Languages : en
Pages : 111
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Book Description
Panel count data arise in event history studies. It may not be feasible to monitor subjects continuously and recurrent events can be observed only at discrete time points rather than continuously and thus only the numbers of the events that occur between the observation times, not their occurrence times, are observed. The resulting interval- censored recurrent event data are commonly referred to as panel count data. The first part of this dissertation discusses a class of semiparametric transformation models for regression analysis of panel count data when the observation times or process may differ from subject to subject and more importantly, may contain relevant information about the underlying recurrent event. The second part of this dissertation will consider semiparametric transformation models for regression analysis of multivariate panel count data. The last part of the dissertation considers the same problem studied in Chapter 2 and provides an approach that allows both observation and follow-up times to be correlated with the recurrent event process. In all three parts, extensive simulation studies were conducted and indicate that the proposed approaches work well for practical situations. Illustrative examples are provided.
Author: Jianguo Sun
Publisher: Springer Science & Business Media
ISBN: 1461487153
Category : Medical
Languages : en
Pages : 283
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Book Description
Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.
Author: Guanglei Yu
Publisher:
ISBN:
Category :
Languages : en
Pages : 108
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Book Description
Recurrent event data and panel count data are two common types of data that have been studied extensively in event history studies in literature. By recurrent event data, we mean that subjects are observed continuously in the follow-up study and thus occurrence times of recurrent events of interest are available. For panel count data, subjects are monitored periodically at discrete observation times and thus only numbers of recurrent events between two subsequent observations are recorded. In addition, one may face mixed panel count data in practice, which are the mixture of recurrent event data and panel count data. They arise when each study subject may be observed continuously during the whole study period, continuously over some study periods and at some time points otherwise, or only at some discrete time points. That is, these mixed data provide complete or incomplete information on the recurrent event process over different time periods for different subjects. It is well-known that in panel count data, the observation process may carry information on the underlying recurrent event process and the censoring may also be dependent in practice. Under such circumstance, the first part of this dissertation will discuss regression analysis of panel count data with informative observations and drop-outs. For the problem, a general means model is presented that can allow both additive and multiplicative effects of covariates on the underlying recurrent event process. In addition, the proportional rates model and the accelerated failure time model are employed to describe the covariate effects on the observation process and the dropout or follow-up process, respectively. For estimation of regression parameters, some estimating equation-based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for the estimation of the covariance matrix of the proposed estimator and a model checking procedure is also provided. The results from an extensive simulation study indicate that the proposed methodology works well for practical situations and it is applied to a motivated set of real data from the Childhood Cancer Survivor Study (CCSS) given in Section 1.1.2.2. In the second part of this dissertation, we will consider regression analysis of mixed panel count data. One major problem in the statistical inference on the mixed data is to combine these two different types of data structures. Since panel count data can be viewed as interval-censored recurrent event data with exact occurrence times of events of interest unobserved or missing, they may be augmented by filling in those missing data by imputation. Then the mixed data can be converted to recurrent event data on which the existing statistical inference method can be easily implemented. Motivated by this, a multiple imputation-based estimation approach is proposed. A simulation study is conducted to study the finite-sample properties of the proposed methodology and it shows that the proposed method is more efficient than the existing method. Also, an illustrative example from the CCSS is provided. The third part of this dissertation still considers regression analysis of mixed panel count data but in the presence of a dependent terminal event, which precludes further occurrence of either recurrent events of interest or observations. For this problem, we present a marginal modeling approach which acknowledges the fact that there will be no more recurrent events after the terminal event and leaves the correlation structure unspecified. To estimate the parameters of interest, an estimating equation-based procedure is developed and the inverse probability of survival weighting technique is used. Asymptotic properties of proposed estimators are also established and finite-sample properties are assessed in a simulation study. We again apply this proposed methodology to the CCSS. In the last part of this dissertation, we will discuss some work directions of the future research.
Author: Yang Li
Publisher:
ISBN:
Category : Biometry
Languages : en
Pages : 115
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Book Description
Panel count data are one type of event-history data concerning recurrent events. Ideally for an event-history study, subjects should be monitored continuously, so for the events that may happen recurrently over time, the exact time of each event occurrence is recordable. Data obtained in such cases are commonly referred to as recurrent event data (Cook and Lawless, 2007). In reality, however, subjects may only be observed at their clinical visits or discrete times. As a result, instead of observing the exact event times, one only knows the numbers of events that happen between the observation times. Such interval-censored recurrent event data are usually referred to as panel count data (Kalbfleisch and Lawless, 1985; Sun and Kalbfleisch, 1995; Thall and Lachin, 1988). The primary interest with panel count data is about the underlying recurrent event process. Meanwhile for the analysis, one needs to consider the times when the observations occur, which can be regarded as realizations of an observation process with follow-up times. This dissertation consists of four parts. In the first part, we will consider regression analysis of panel count data with dependent observation processes while the follow-up times may be subject to a terminal event like death. A semiparametric transformation model is presented for the mean function of the underlying recurrent event process among survivals. To estimate the regression parameters, an estimating equation approach is proposed and the inverse survival probability weighting technique is used. In addition, the asymptotic distribution of the proposed estimate is derived and a model checking procedure is presented. Simulation studies are conducted to evaluate finite sample properties of the proposed approach, and the approach is applied to a bladder cancer study. The second part will focus on regression analysis of multivariate panel count data in the presence of a terminal event. Both the observation process and the terminal event may be correlated with recurrent event processes of interest. We present a class of semiparametric additive models for the mean functions of the underlying recurrent event processes. For the estimation of the regression parameters, an estimating equation based inference procedure is developed. The asymptotic properties of the proposed estimators are established and a model-checking procedure is derived for practical situations. The third part will discuss nonparametric comparison based on panel count data. Most approaches that have been developed in the literature require an equal observation process for all subjects. However, such an assumption may not hold in reality. A new class of test procedures are proposed that allow unequal observation processes for the subjects from different treatment groups, and both univariate and multivariate panel count data are considered. The asymptotic normality of the proposed test statistics is established and a simulation study is conducted. The approach is applied to a skin cancer study. Finally, the last part will discuss some directions for future research.
Author: Do-Hwan Park
Publisher:
ISBN:
Category : Longitudinal method
Languages : en
Pages : 198
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Book Description
Author: Zhezhen Jin
Publisher: Springer
ISBN: 3319425714
Category : Medical
Languages : en
Pages : 218
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Book Description
The papers in this volume represent the most timely and advanced contributions to the 2014 Joint Applied Statistics Symposium of the International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS), held in Portland, Oregon. The contributions cover new developments in statistical modeling and clinical research: including model development, model checking, and innovative clinical trial design and analysis. Each paper was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. It offered 3 keynote speeches, 7 short courses, 76 parallel scientific sessions, student paper sessions, and social events.
Author: Badi Hani Baltagi
Publisher:
ISBN: 0199940045
Category : Business & Economics
Languages : en
Pages : 705
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Book Description
The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
Author: Jianguo Sun
Publisher: Springer
ISBN: 0387371192
Category : Mathematics
Languages : en
Pages : 310
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Book Description
This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic of interval-censored data and complements the books on right-censored data. The focus of the book is on nonparametric and semiparametric inferences, but it also describes parametric and imputation approaches. This book provides an up-to-date reference for people who are conducting research on the analysis of interval-censored failure time data as well as for those who need to analyze interval-censored data to answer substantive questions.
Author: Lászlo Mátyás
Publisher: Springer Science & Business Media
ISBN: 3540758925
Category : Business & Economics
Languages : en
Pages : 966
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Book Description
This restructured, updated Third Edition provides a general overview of the econometrics of panel data, from both theoretical and applied viewpoints. Readers discover how econometric tools are used to study organizational and household behaviors as well as other macroeconomic phenomena such as economic growth. The book contains sixteen entirely new chapters; all other chapters have been revised to account for recent developments. With contributions from well known specialists in the field, this handbook is a standard reference for all those involved in the use of panel data in econometrics.
