Statistical Methods for Survival Data Analysis

Statistical Methods for Survival Data Analysis

Praise for the Third Edition “. . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject.” —Statistics in Medical Research Updated and expanded to reflect the latest ...

Author: Elisa T. Lee

Publisher: John Wiley & Sons

ISBN: 9781118593059

Category: Mathematics

Page: 512

View: 479

Praise for the Third Edition “. . . an easy-to read introduction to survival analysiswhich covers the major concepts and techniques of thesubject.” —Statistics in Medical Research Updated and expanded to reflect the latest developments,Statistical Methods for Survival Data Analysis, FourthEdition continues to deliver a comprehensive introduction tothe most commonly-used methods for analyzing survival data.Authored by a uniquely well-qualified author team, the FourthEdition is a critically acclaimed guide to statistical methods withapplications in clinical trials, epidemiology, areas of business,and the social sciences. The book features many real-world examplesto illustrate applications within these various fields, althoughspecial consideration is given to the study of survival data inbiomedical sciences. Emphasizing the latest research and providing the mostup-to-date information regarding software applications in thefield, Statistical Methods for Survival Data Analysis, FourthEdition also includes: Marginal and random effect models for analyzing correlatedcensored or uncensored data Multiple types of two-sample and K-sample comparisonanalysis Updated treatment of parametric methods for regression modelfitting with a new focus on accelerated failure time models Expanded coverage of the Cox proportional hazards model Exercises at the end of each chapter to deepen knowledge of thepresented material Statistical Methods for Survival Data Analysis is anideal text for upper-undergraduate and graduate-level courses onsurvival data analysis. The book is also an excellent resource forbiomedical investigators, statisticians, and epidemiologists, aswell as researchers in every field in which the analysis ofsurvival data plays a role.
Categories: Mathematics

Statistical Methods for Survival Data Analysis

Statistical Methods for Survival Data Analysis

Third Edition brings the text up to date with new material and updated references.

Author: Elisa T. Lee

Publisher:

ISBN: UOM:39015028048919

Category: Clinical trials

Page: 557

View: 359

Third Edition brings the text up to date with new material and updated references. New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional hazards model (including stratification and time-dependent covariates); and multiple responses to the logistic regression model. Coverage of graphical methods has been deleted. Large data sets are provided on an FTP site for readers' convenience. Bibliographic remarks conclude each chapter.
Categories: Clinical trials

Handbook of Survival Analysis

Handbook of Survival Analysis

With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches.

Author: John P. Klein

Publisher: CRC Press

ISBN: 9781466555679

Category: Mathematics

Page: 656

View: 622

Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Categories: Mathematics

Counting Processes and Survival Analysis

Counting Processes and Survival Analysis

This text [is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis." —Short Book Reviews, International Statistical Institute Counting Processes and Survival Analysis explores the ...

Author: Thomas R. Fleming

Publisher: John Wiley & Sons

ISBN: 9781118150665

Category: Mathematics

Page: 448

View: 458

The Wiley-Interscience Paperback Series consists of selected booksthat have been made more accessible to consumers in an effort toincrease global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "The book is a valuable completion of the literature in this field.It is written in an ambitious mathematical style and can berecommended to statisticians as well as biostatisticians." -Biometrische Zeitschrift "Not many books manage to combine convincingly topics fromprobability theory over mathematical statistics to appliedstatistics. This is one of them. The book has other strong pointsto recommend it: it is written with meticulous care, in a lucidstyle, general results being illustrated by examples fromstatistical theory and practice, and a bunch of exercises serve tofurther elucidate and elaborate on the text." -Mathematical Reviews "This book gives a thorough introduction to martingale and countingprocess methods in survival analysis thereby filling a gap in theliterature." -Zentralblatt für Mathematik und ihre Grenzgebiete/MathematicsAbstracts "The authors have performed a valuable service to researchers inproviding this material in [a] self-contained and accessible form.. . This text [is] essential reading for the probabilist ormathematical statistician working in the area of survivalanalysis." -Short Book Reviews, International Statistical Institute Counting Processes and Survival Analysis explores the martingaleapproach to the statistical analysis of counting processes, with anemphasis on the application of those methods to censored failuretime data. This approach has proven remarkably successful inyielding results about statistical methods for many problemsarising in censored data. A thorough treatment of the calculus ofmartingales as well as the most important applications of thesemethods to censored data is offered. Additionally, the bookexamines classical problems in asymptotic distribution theory forcounting process methods and newer methods for graphical analysisand diagnostics of censored data. Exercises are included to providepractice in applying martingale methods and insight into thecalculus itself.
Categories: Mathematics

Survival Models and Data Analysis

Survival Models and Data Analysis

This book, originally published in 1980, surveys and analyzes methods that use survival measurements and concepts, and helps readers apply the appropriate method for a given situation.

Author: Regina C. Elandt-Johnson

Publisher: John Wiley & Sons

ISBN: 9781119011033

Category: Mathematics

Page: 480

View: 835

Survival analysis deals with the distribution of life times, essentially the times from an initiating event such as birth or the start of a job to some terminal event such as death or pension. This book, originally published in 1980, surveys and analyzes methods that use survival measurements and concepts, and helps readers apply the appropriate method for a given situation. Four broad sections cover introductions to data, univariate survival function, multiple-failure data, and advanced topics.
Categories: Mathematics

Modeling Survival Data Extending the Cox Model

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.

Author: Terry M. Therneau

Publisher: Springer Science & Business Media

ISBN: 9781475732948

Category: Mathematics

Page: 350

View: 345

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.
Categories: Mathematics

Dynamic Regression Models for Survival Data

Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables.

Author: Torben Martinussen

Publisher: Springer Science & Business Media

ISBN: 9780387339603

Category: Medical

Page: 470

View: 597

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables. Use of the suggested models and methods is illustrated on real data examples, using the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets.
Categories: Medical

An Introduction to Survival Analysis Using Stata Second Edition

An Introduction to Survival Analysis Using Stata  Second Edition

This book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ..."--Book jacket.

Author: Mario Cleves

Publisher: Stata Press

ISBN: 9781597180412

Category: Computers

Page: 372

View: 421

"[This book] provides new researchers with the foundation for understanding the various approaches for analyzing time-to-event data. This book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ..."--Book jacket.
Categories: Computers

Dynamic Prediction in Clinical Survival Analysis

Dynamic Prediction in Clinical Survival Analysis

Hans C. van Houwelingen received his Ph.D. in mathematical statistics from the
Unversity of Utrecht in 1973. ... of Statistica Neerlandica and served on the
editorial boards of Statistical Methods in Medical Research, Lifetime Data
Analysis ...

Author: Hans van Houwelingen

Publisher: CRC Press

ISBN: 9781439835432

Category: Mathematics

Page: 250

View: 977

There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime after diagnosis or treatment. In contrast, Dynamic Prediction in Clinical Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to be useful to applied statisticians and clinical epidemiologists, each chapter in the book has a practical focus on the issues of working with real life data. Chapters conclude with additional material either on the interpretation of the models, alternative models, or theoretical background. The book consists of four parts: Part I deals with prognostic models for survival data using (clinical) information available at baseline, based on the Cox model Part II is about prognostic models for survival data using (clinical) information available at baseline, when the proportional hazards assumption of the Cox model is violated Part III is dedicated to the use of time-dependent information in dynamic prediction Part IV explores dynamic prediction models for survival data using genomic data Dynamic Prediction in Clinical Survival Analysis summarizes cutting-edge research on the dynamic use of predictive models with traditional and new approaches. Aimed at applied statisticians who actively analyze clinical data in collaboration with clinicians, the analyses of the different data sets throughout the book demonstrate how predictive models can be obtained from proper data sets.
Categories: Mathematics

Survival Analysis Using S

Survival Analysis Using S

Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology.

Author: Mara Tableman

Publisher: CRC Press

ISBN: 0203501411

Category: Mathematics

Page: 280

View: 135

Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.
Categories: Mathematics

Applied Survival Analysis Using R

Applied Survival Analysis Using R

This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis.

Author: Dirk F. Moore

Publisher: Springer

ISBN: 9783319312453

Category: Medical

Page: 226

View: 267

Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.
Categories: Medical

Survival Analysis

Survival Analysis

A concise summary of the statistical methods used in the analysis of survival data with censoring.

Author: Rupert G. Miller, Jr.

Publisher: John Wiley & Sons

ISBN: 9781118031063

Category: Mathematics

Page: 238

View: 264

A concise summary of the statistical methods used in the analysis of survival data with censoring. Emphasizes recently developed nonparametric techniques. Outlines methods in detail and illustrates them with actual data. Discusses the theory behind each method. Includes numerous worked problems and numerical exercises.
Categories: Mathematics

Fundamental Statistical Methods for Analysis of Alzheimer s and Other Neurodegenerative Diseases

Fundamental Statistical Methods for Analysis of Alzheimer s and Other Neurodegenerative Diseases

“R2MLwiN: A package to run MLwiN from within R.” Journal of Statistical
Software 72(10): 1–43. Zhu, M. (2014). Analyzing multilevel models with the
GLIMMIX procedure. Washington, DC: SAS Global Forum. Chapter 8. Survival
Data Analysis ...

Author: Katherine E. Irimata

Publisher: JHU Press

ISBN: 9781421436722

Category: Medical

Page: 480

View: 238

Alzheimer's disease is a devastating condition that presents overwhelming challenges to patients and caregivers. In the face of this relentless and as-yet incurable disease, mastery of statistical analysis is paramount for anyone who must assess complex data that could improve treatment options. This unique book presents up-to-date statistical techniques commonly used in the analysis of data on Alzheimer's and other neurodegenerative diseases. With examples drawn from the real world that will make it accessible to disease researchers, practitioners, academics, and students alike, this volume • presents code for analyzing dementia data in statistical programs, including SAS, R, SPSS, and Stata • introduces statistical models for a range of data types, including continuous, categorical, and binary responses, as well as correlated data • draws on datasets from the National Alzheimer's Coordinating Center, a large relational database of standardized clinical and neuropathological research data • discusses advanced statistical methods, including hierarchical models, survival analysis, and multiple-membership • examines big data analytics and machine learning methods Easy to understand but sophisticated in its approach, Fundamental Statistical Methods for Analysis of Alzheimer's and Other Neurodegenerative Diseases will be a cornerstone for anyone looking for simplicity in understanding basic and advanced statistical data analysis topics. Allowing more people to aid in analyzing data—while promoting constructive dialogues with statisticians—this book will hopefully play an important part in unlocking the secrets of these confounding diseases.
Categories: Medical

Advanced Statistical Methods in Data Science

Advanced Statistical Methods in Data Science

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015.

Author: Ding-Geng Chen

Publisher: Springer

ISBN: 9789811025945

Category: Mathematics

Page: 222

View: 819

This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.
Categories: Mathematics

Survival Analysis

Survival Analysis

Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques.

Author: Xian Liu

Publisher: John Wiley & Sons

ISBN: 9781118307670

Category: Mathematics

Page: 427

View: 778

Survival analysis concerns sequential occurrences of events governed by probabilistic laws. Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis. Assumes only a minimal knowledge of SAS whilst enabling more experienced users to learn new techniques of data input and manipulation. Provides numerous examples of SAS code to illustrate each of the methods, along with step-by-step instructions to perform each technique. Highlights the strengths and limitations of each technique covered. Covering a wide scope of survival techniques and methods, from the introductory to the advanced, this book can be used as a useful reference book for planners, researchers, and professors who are working in settings involving various lifetime events. Scientists interested in survival analysis should find it a useful guidebook for the incorporation of survival data and methods into their projects.
Categories: Mathematics

Analysis of Failure and Survival Data

Analysis of Failure and Survival Data

Mastering the contents of this book will help prepare students to begin performing research in survival analysis and reliability and provide seasoned practitioners with a deeper understanding of the field.

Author: Peter J. Smith

Publisher: CRC Press

ISBN: 9781351989671

Category: Mathematics

Page: 266

View: 457

Analysis of Failure and Survival Data is an essential textbook for graduate-level students of survival analysis and reliability and a valuable reference for practitioners. It focuses on the many techniques that appear in popular software packages, including plotting product-limit survival curves, hazard plots, and probability plots in the context of censored data. The author integrates S-Plus and Minitab output throughout the text, along with a variety of real data sets so readers can see how the theory and methods are applied. He also incorporates exercises in each chapter that provide valuable problem-solving experience. In addition to all of this, the book also brings to light the most recent linear regression techniques. Most importantly, it includes a definitive account of the Buckley-James method for censored linear regression, found to be the best performing method when a Cox proportional hazards method is not appropriate. Applying the theories of survival analysis and reliability requires more background and experience than students typically receive at the undergraduate level. Mastering the contents of this book will help prepare students to begin performing research in survival analysis and reliability and provide seasoned practitioners with a deeper understanding of the field.
Categories: Mathematics

Statistics for Censored Environmental Data Using Minitab and R

Statistics for Censored Environmental Data Using Minitab and R

This new edition applies methods of survival analysis, including methods for interval-censored data to the interpretation of low-level contaminants in environmental sciences and occupational health.

Author: Dennis R. Helsel

Publisher: John Wiley & Sons

ISBN: 9781118162767

Category: Mathematics

Page: 344

View: 360

Praise for the First Edition " . . . an excellent addition to an upper-level undergraduatecourse on environmental statistics, and . . . a 'must-have' deskreference for environmental practitioners dealing with censoreddatasets." —Vadose Zone Journal Statistical Methods for Censored Environmental Data UsingMinitab® and R, Second Edition introduces and explains methodsfor analyzing and interpreting censored data in the environmentalsciences. Adapting survival analysis techniques from other fields,the book translates well-established methods from other disciplinesinto new solutions for environmental studies. This new edition applies methods of survival analysis, includingmethods for interval-censored data to the interpretation oflow-level contaminants in environmental sciences and occupationalhealth. Now incorporating the freely available R software as wellas Minitab® into the discussed analyses, the book featuresnewly developed and updated material including: A new chapter on multivariate methods for censored data Use of interval-censored methods for treating true nondetects aslower than and separate from values between the detection andquantitation limits ("remarked data") A section on summing data with nondetects A newly written introduction that discusses invasive data,showing why substitution methods fail Expanded coverage of graphical methods for censored data The author writes in a style that focuses on applications ratherthan derivations, with chapters organized by key objectives such ascomputing intervals, comparing groups, and correlation. Examplesaccompany each procedure, utilizing real-world data that can beanalyzed using the Minitab® and R software macros available onthe book's related website, and extensive references direct readersto authoritative literature from the environmental sciences. Statistics for Censored Environmental Data Using Minitab®and R, Second Edition is an excellent book for courses onenvironmental statistics at the upper-undergraduate and graduatelevels. The book also serves as a valuable referencefor¿environmental professionals, biologists, and ecologistswho focus on the water sciences, air quality, and soil science.
Categories: Mathematics

Longitudinal Data Analysis

Longitudinal Data Analysis

LE - Applied Categorical Data Analysis LE 1 Applied Survival Analysis LEE and
WANG 1 Statistical Methods for Survival Data Analysis, Third Edition LEPAGE
and BILLARD 1 Exploring the Limits of Bootstrap LEYLAND and GOLDSTEIN ...

Author: Donald Hedeker

Publisher: John Wiley & Sons

ISBN: 9780470036471

Category: Mathematics

Page: 360

View: 403

Longitudinal data analysis for biomedical and behavioral sciences This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data. Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include: * Repeated measures analysis of variance * Multivariate analysis of variance for repeated measures * Random-effects regression models (RRM) * Covariance-pattern models * Generalized-estimating equations (GEE) models * Generalizations of RRM and GEE for categorical outcomes Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge. This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.
Categories: Mathematics