The Frailty Model

Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data.

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Author: Luc Duchateau

Publisher: Springer Science & Business Media

ISBN: 9780387728353

Category: Mathematics

Page: 316

View: 890

Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.

Statistical Modelling of Survival Data with Random Effects

This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in ...

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Author: Il Do Ha

Publisher: Springer

ISBN: 9789811065576

Category: Mathematics

Page: 283

View: 627

This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.

Analysis of Multivariate Survival Data

This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail.

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Author: Philip Hougaard

Publisher: Springer Science & Business Media

ISBN: 9781461213048

Category: Mathematics

Page: 542

View: 821

Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. As the field is rather new, the concepts and the possible types of data are described in detail. Four different approaches to the analysis of such data are presented from an applied point of view.

Survival and Event History Analysis

... models include the EM-algorithm, penalised likelihood techniques, Laplacian integration and Bayesian techniques. More advanced frailty models for hierarchical data are also included. 2008. 335 pp. (Statistics for Biology and Health) ...

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Author: Odd Aalen

Publisher: Springer Science & Business Media

ISBN: 9780387685601

Category: Mathematics

Page: 540

View: 543

The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.

Mixed Effects Models for the Population Approach

Maximum likelihood from incomplete data via the EM algorithm. ... Information methods for model selection in linear mixed effects models with application to HCV data. ... The Frailty Model. Statistics for Biology and Health. Springer.

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Author: Marc Lavielle

Publisher: CRC Press

ISBN: 9781482226515

Category: Mathematics

Page: 383

View: 256

Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data Modeling The book first shows how the framework allows model representation for different data types, including continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM), for modeling these diverse data types. The book also covers other essential methods, including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix, and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical Implementation This book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs.

Design and Analysis of Clinical Trials with Time to Event Endpoints

Testing equality of survival functions based on both paired and unpaired censored data. Biometrics, 56:154–159, 2000. L. Duchateau and P. Janssen. The Frailty Model, 1st edn. Statistics for Biology and Health. Springer, New York, 2007.

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Author: Karl E. Peace

Publisher: CRC Press

ISBN: 9781420066401

Category: Mathematics

Page: 616

View: 597

Using time-to-event analysis methodology requires careful definition of the event, censored observation, provision of adequate follow-up, number of events, and independence or "noninformativeness" of the censoring mechanisms relative to the event. Design and Analysis of Clinical Trials with Time-to-Event Endpoints provides a thorough presentation of the design, monitoring, analysis, and interpretation of clinical trials in which time-to-event is of critical interest. After reviewing time-to-event endpoint methodology, clinical trial issues, and the design and monitoring of clinical trials, the book focuses on inferential analysis methods, including parametric, semiparametric, categorical, and Bayesian methods; an alternative to the Cox model for small samples; and estimation and testing for change in hazard. It then presents descriptive and graphical methods useful in the analysis of time-to-event endpoints. The next several chapters explore a variety of clinical trials, from analgesic, antibiotic, and antiviral trials to cardiovascular and cancer prevention, prostate cancer, astrocytoma brain tumor, and chronic myelogonous leukemia trials. The book then covers areas of drug development, medical practice, and safety assessment. It concludes with the design and analysis of clinical trials of animals required by the FDA for new drug applications. Drawing on the expert contributors’ experiences working in biomedical research and clinical drug development, this comprehensive resource covers an array of time-to-event methods and explores an assortment of real-world applications.

Modeling Survival Data Extending the Cox Model

This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data.

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Author: Terry M. Therneau

Publisher: Springer Science & Business Media

ISBN: 9781475732948

Category: Mathematics

Page: 350

View: 858

This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.

Statistical and Methodological Aspects of Oral Health Research

Statistics for Biology and Health. New York, Springer. [32] D. Lin, L. Wei & Z. Ying (1993) Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika, 80(3), 557–72. [33] P. Hougaard (2000) Analysis of ...

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Author: Emmanuel Lesaffre

Publisher: John Wiley & Sons

ISBN: 047074412X

Category: Mathematics

Page: 408

View: 367

Statistical and Methodological Aspects of Oral Health Research provides oral health researchers with an overview of the methodological aspects that are important in planning, conducting and analyzing their research projects whilst also providing biostatisticians with an idea of the statistical problems that arise when tackling oral health research questions. This collection presents critical reflections on oral health research and offers advice on practical aspects of setting up research whilst introducing the reader to basic as well as advanced statistical methodology. Features: An introduction to research methodology and an exposition of the state of the art. A variety of examples from oral health research. Contributions from well-known oral health researchers, epidemiologists and biostatisticians, all of whom have rich experience in this area. Recent developments in statistical methodology prompted by a variety of dental applications. Presenting both an introduction to research methodology and an exposition of the latest advances in oral health research, this book will appeal both beginning and experienced oral health researchers as well as biostatisticians and epidemiologists.

Bayesian Survival Analysis

Terry M. Thermeau Patriciu M. Grumbach Modeling Survival Extending the Cox Model ... HARDCOWER ISBN 0-387-38784-3 STATISTICS FOR BIOLOGY AND HEALTH Philip Hougaard Analysis of Multivariate Survival Data Data PHILIP HOUGAARD ANALYSIS OF ...

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Author: Joseph G. Ibrahim

Publisher: Springer Science & Business Media

ISBN: 9781475734478

Category: Medical

Page: 480

View: 287

Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all from the health sciences, including cancer, AIDS, and the environment.

Handbook of Infectious Disease Data Analysis

A new measure of time-varying association for shared frailty models with bivariate current status data. Biostatistics, 13(4):665–679, 2012. [8] R. M. Anderson and R. M. ... Statistics for Biology and Health. Springer, New York, 2006.

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Author: Leonhard Held

Publisher: CRC Press

ISBN: 9781351839310

Category: Medical

Page: 554

View: 371

Recent years have seen an explosion in new kinds of data on infectious diseases, including data on social contacts, whole genome sequences of pathogens, biomarkers for susceptibility to infection, serological panel data, and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered. Key features Contributors include many leading researchers in the field Divided into four main sections: Basic concepts, Analysis of Outbreak Data, Analysis of Seroprevalence Data, Analysis of Surveillance Data Numerous case studies and examples throughout Provides both introductory material and key reference material