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多元时间数据的统计分析:一种边际建模方法-The statistical analysis of multivariate time data: a marginal modeling approac

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标题(title):The statistical analysis of multivariate time data: a marginal modeling approach
多元时间数据的统计分析:一种边际建模方法
作者(author):Prentice, Ross L.; Zhao, Shanshan
出版社(publisher):CRC Press
大小(size):9 MB (9445712 bytes)
格式(extension):pdf
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The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women's Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.  Read more...
Abstract: The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women's Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina
Table of contents :
Content: 1. Introduction and Characterization of Multivariate Failure Time DistributionsFailure Time Data and Distributions Bivariate Failure Time Data and Distributions Bivariate Failure Time Regression Modeling Higher Dimensional Failure Time Data and Distributions Multivariate Response Data: Modeling and Analysis Recurrent Event Characterization and Modeling Some Application Settings Aplastic anemia clinical trial Australian twin data Women's Health Initiative hormone therapy trials Bladder tumor recurrence data Women's Health Initiative dietary modification trial 2. Univariate Failure Time Data Analysis Methods Overview Nonparametric Survivor Function Estimation Hazard Ratio Regression Estimation Using the Cox Model Cox Model Properties and Generalizations Censored Data Rank Tests Cohort Sampling and Dependent Censoring Aplastic Anemia Clinical Trial Application WHI Postmenopausal Hormone Therapy Application Asymptotic Distribution Theory Additional Univariate Failure Time Models and Methods Cox-Logistic Model for Failure Time Data 3. Nonparametric Estimation of the Bivariate Survivor Function Introduction Plug-In Nonparametric Estimators of F The Volterra estimator The Dabrowska and Prentice-Cai estimators Simulation evaluation Asymptotic distributional results Maximum Likelihood and Estimating Equation Approaches Nonparametric Assessment of Dependency Cross ratio and concordance function estimators Australian twin study illustration Simulation evaluation Additional Estimators and Estimation Perspectives Additional bivariate survivor function estimators Estimation perspectives 4. Regression Analysis of Bivariate Failure Time Data Introduction Independent Censoring and Likelihood-Based Inference Copula Models and Estimation Methods Formulation Likelihood-based estimation Unbiased estimating equations Frailty Models and Estimation Methods Australian Twin Study Illustration Hazard Rate Regression Semiparametric regression model possibilities Cox models for marginal single and dual outcome hazard rates Dependency measures given covariates Asymptotic distribution theory Simulation evaluation of marginal hazard rate estimators Composite Outcomes in a Low-Fat Diet Trial Counting Process Intensity Modeling Marginal Hazard Rate Regression in Context Likelihood maximization and empirical plug-in estimators Independent censoring and death outcomes Marginal hazard rates for competing risk data Summary 5. Trivariate Failure Time Data Modeling and Analysis Introduction Trivariate Survivor Function Estimation Dabrowska-type Estimator Development Volterra Estimator Trivariate Dependency Assessment Simulation Evaluation and Comparison Trivariate Regression Analysis via Copulas Marginal Hazard Rate Regression Simulation Evaluation of Hazard Ratio Estimators Hormone Therapy and Disease Occurrence 6. Higher Dimensional Failure Time Data Modeling and Estimation Introduction M-dimensional Survivor Function Estimation Dabrowska-type estimator development Volterra nonparametric survivor function estimator Multivariate dependency assessment Single Failure Hazard Rate Regression Regression on Marginal Hazard Rates and Dependencies Likelihood specification Estimation using copula models Marginal Single and Double Failure Hazard Rate Modeling Counting Process Intensity Modeling and Estimation Women's Health Initiative Hormone Therapy Illustration More on Estimating Equations and Likelihood 7. Recurrent Event Data Analysis Methods Introduction Intensity Process Modeling on a Single Failure Time Axis Counting process intensity modeling and estimation Bladder tumor recurrence illustration Intensity modeling with multiple failure types Marginal Failure Rate Estimation with Recurrent Events Single and Double Failure Rate Models for Recurrent Events WHI Dietary Modification Trial Illustration Absolute Failure Rates and Mean Models for Recurrent Events Intensity Versus Marginal Hazard Rate Modeling 8. Additional Important Multivariate Failure Time Topics Introduction Dependent Censorship, Confounding and Mediation Dependent censorship Confounding control and mediation analysis Cohort Sampling and Missing Covariates Introduction Case-cohort and two-phase sampling Nested case-control sampling Missing covariate data methods Mismeasured Covariate Data Background Hazard rate estimation with a validation subsample Hazard rate estimation without a validation subsample Energy intake and physical activity in relation to chronic disease risk Joint Covariate and Failure Rate Modeling Model Checking Marked Point Processes and Multistate Models Imprecisely Measured Failure Times Appendix : Technical Materials A Product Integrals and Steiltjes Integration A Generalized Estimating Equations for Mean Parameters A Some Basic Empirical Process Results Appendix Software and Data A Software for Multivariate Failure Time Analysis A Data Access
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