Modelling Survival Data In Medical Research Second Edition
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Modelling Survival Data In Medical Research
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Author : D. Collett
language : en
Publisher:
Release Date : 2003
Modelling Survival Data In Medical Research written by D. Collett and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with MATHEMATICS categories.
Survival analysis is an active field and many advances, particularly in software, have emerged over the last eight years. Modelling Survival Data in Medical Research, Second Edition updates and expands on the highly successful first edition, which was praised for its clarity, content, and broad-based accessibility. This edition presents the most current and useful modelling techniques in survival data analysis, including recent developments in model checking, parametric models, time-dependent variables, and interval censored data. For this edition, the author has focused the software sections.
Modelling Survival Data In Medical Research Second Edition
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Author : David Collett
language : en
Publisher: CRC Press
Release Date : 2003-03-28
Modelling Survival Data In Medical Research Second Edition written by David Collett and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-03-28 with Mathematics categories.
Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis.The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical practices.
Modelling Survival Data In Medical Research Second Edition
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Author : David Collett
language : en
Publisher:
Release Date : 2003
Modelling Survival Data In Medical Research Second Edition written by David Collett and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.
Applied Survival Analysis
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Author : David W. Hosmer, Jr.
language : en
Publisher: John Wiley & Sons
Release Date : 2008-03-07
Applied Survival Analysis written by David W. Hosmer, Jr. and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-07 with Mathematics categories.
THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.
Modelling Survival Data In Medical Research
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Author : David Collett
language : en
Publisher: CRC Press
Release Date : 2015-05-04
Modelling Survival Data In Medical Research written by David Collett and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-04 with Mathematics categories.
Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range of examples from biomedical research.Well known for its nontechnical style, this third edition contains new chapters on frailty models and their applications, competing risks, non-proportional hazards, and dependent censo
Survival Analysis
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Author : David Machin
language : en
Publisher: John Wiley & Sons
Release Date : 2006-03-30
Survival Analysis written by David Machin and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-30 with Medical categories.
Well received in its first edition, Survival Analysis: A Practical Approach is completely revised to provide an accessible and practical guide to survival analysis techniques in diverse environments. Illustrated with many authentic examples, the book introduces basic statistical concepts and methods to construct survival curves, later developing them to encompass more specialised and complex models. During the years since the first edition there have been several new topics that have come to the fore and many new applications. Parallel developments in computer software programmes, used to implement these methodologies, are relied upon throughout the text to bring it up to date.
Modeling Survival Data Extending The Cox Model
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Author : Terry M. Therneau
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Modeling Survival Data Extending The Cox Model written by Terry M. Therneau and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
Extending the Cox Model is aimed at researchers, practitioners, and graduate students who have some exposure to traditional methods of survival analysis. The emphasis is on semiparametric methods based on the proportional hazards model. The inclusion of examples with SAS and S-PLUS code will make the book accessible to most working statisticians.
Survival Analysis
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Author : John P. Klein
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Survival Analysis written by John P. Klein and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Medical categories.
Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology. "This book...offers an excellent course in survival analysis for
Modeling Survival Data Using Frailty Models
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Author : David D. Hanagal
language : en
Publisher: Springer Nature
Release Date : 2019-11-16
Modeling Survival Data Using Frailty Models written by David D. Hanagal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-16 with Medical categories.
This book presents the basic concepts of survival analysis and frailty models, covering both fundamental and advanced topics. It focuses on applications of statistical tools in biology and medicine, highlighting the latest frailty-model methodologies and applications in these areas. After explaining the basic concepts of survival analysis, the book goes on to discuss shared, bivariate, and correlated frailty models and their applications. It also features nine datasets that have been analyzed using the R statistical package. Covering recent topics, not addressed elsewhere in the literature, this book is of immense use to scientists, researchers, students and teachers.
Bayesian Survival Analysis
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Author : Joseph G. Ibrahim
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Bayesian Survival Analysis written by Joseph G. Ibrahim and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Medical categories.
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. Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions. The book 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 essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.