Survival and Event History Analysis

A Process Point of View

Author: Odd Aalen,Ornulf Borgan,Hakon Gjessing

Publisher: Springer Science & Business Media

ISBN: 038768560X

Category: Mathematics

Page: 540

View: 2404

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.

Introducing Survival and Event History Analysis

Author: Melinda Mills

Publisher: SAGE Publications

ISBN: 1848601026

Category: Social Science

Page: 279

View: 6464

This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences. Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ‘hands-on’ exercises, conversations with key scholars and resources for both students and instructors, this text allows researchers to quickly master advanced statistical techniques. It is written from the perspective of the ‘user’, making it suitable as both a self-learning tool and graduate-level textbook. Also included are up-to-date innovations in the field, including advancements in the assessment of model fit, unobserved heterogeneity, recurrent events and multilevel event history models. Practical instructions are also included for using the statistical programs of R, STATA and SPSS, enabling readers to replicate the examples described in the text.

Event History Analysis with R

Author: Göran Broström

Publisher: CRC Press

ISBN: 1439831645

Category: Mathematics

Page: 236

View: 5058

With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using real-life examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times. Features Introduces parametric proportional hazards models with baseline distributions like the Weibull, Gompertz, Lognormal, and Piecewise constant hazard distributions, in addition to traditional Cox regression Presents mathematical details as well as technical material in an appendix Includes real examples with applications in demography, econometrics, and epidemiology Provides a dedicated R package, eha, containing special treatments, including making cuts in the Lexis diagram, creating communal covariates, and creating period statistics A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.

Survival and Event History Analysis

Author: Niels Keiding,Per Kragh Andersen

Publisher: Wiley

ISBN: 9780470058060

Category: Mathematics

Page: 550

View: 3793

A unique and invaluable reference resource for those working in survival analysis. Survival analysis is concerned with studying the time between entry to a study and a subsequent event. Originally the analysis was concerned with time from treatment until death, hence the name, but survival analysis is applicable to many areas in addition to mortality, such as recidivism or the efficacy of drugs. The techniques can also be used in engineering and quality control (e.g. how long is a particular component likely to last?). Arranged in an A-Z format, Survival and Event History Analysis edited by Niels Keiding and Per Kragh Andersen contains 96 articles written by over 60 distinguished authors. The articles are taken from the Encyclopedia of Biostatistics, 2nd Edition. This book gives the reader a thorough grounding in the subject area and the extensive references at the end of each article provide a comprehensive source of information for further information in more depth.

Statistical Models Based on Counting Processes

Author: PER KRAGH ANDERSEN,Ornulf Borgan,Richard D. Gill,Niels Keiding

Publisher: Springer Science & Business Media

ISBN: 1461243483

Category: Mathematics

Page: 784

View: 3818

Modern survival analysis and more general event history analysis may be effectively handled within the mathematical framework of counting processes. This book presents this theory, which has been the subject of intense research activity over the past 15 years. The exposition of the theory is integrated with careful presentation of many practical examples, drawn almost exclusively from the authors'own experience, with detailed numerical and graphical illustrations. Although Statistical Models Based on Counting Processes may be viewed as a research monograph for mathematical statisticians and biostatisticians, almost all the methods are given in concrete detail for use in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuarial mathematicians, reliability engineers and biologists). Much of the material has so far only been available in the journal literature (if at all), and so a wide variety of researchers will find this an invaluable survey of the subject.

Event History Modeling

A Guide for Social Scientists

Author: Janet M. Box-Steffensmeier,Janet M.. Box-Steffensmeier,Bradford S. Jones

Publisher: Cambridge University Press

ISBN: 9780521546737

Category: Political Science

Page: 218

View: 6938

Event History Modeling provides an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences. The authors explain the foundational principles of event-history analysis, and analyse numerous examples which they estimate and interpret using standard statistical packages, such as STATA and S-Plus. They review recent and critical innovations in diagnostics, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. They also discuss common problems encountered with time-to-event data, and make recommendations regarding the implementation of duration modeling methods.

Analysis of Binary Data, Second Edition

Author: D.R. Cox,E. J. Snell

Publisher: CRC Press

ISBN: 9780412306204

Category: Mathematics

Page: 240

View: 6352

The first edition of this book (1970) set out a systematic basis for the analysis of binary data and in particular for the study of how the probability of 'success' depends on explanatory variables. The first edition has been widely used and the general level and style have been preserved in the second edition, which contains a substantial amount of new material. This amplifies matters dealt with only cryptically in the first edition and includes many more recent developments. In addition the whole material has been reorganized, in particular to put more emphasis on m.aximum likelihood methods. There are nearly 60 further results and exercises. The main points are illustrated by practical examples, many of them not in the first edition, and some general essential background material is set out in new Appendices.

Modeling Survival Data: Extending the Cox Model

Author: Terry M. Therneau,Patricia M. Grambsch

Publisher: Springer Science & Business Media

ISBN: 1475732945

Category: Mathematics

Page: 350

View: 5784

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.

Advances in Statistical Methods for the Health Sciences

Applications to Cancer and AIDS Studies, Genome Sequence Analysis, and Survival Analysis

Author: Jean-Louis Auget,N. Balakrishnan,Mounir Mesbah,Geert Molenberghs

Publisher: Springer Science & Business Media

ISBN: 9780817645427

Category: Mathematics

Page: 540

View: 5356

Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained comprehensive volume covers a wide range of topics pertaining to new statistical methods in the health sciences, including epidemiology, pharmacovigilance, quality of life, survival analysis, and genomics. The book will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics.

Event History Analysis

Regression for Longitudinal Event Data

Author: Paul D. Allison

Publisher: SAGE

ISBN: 9780803920552

Category: Social Science

Page: 87

View: 2837

Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.

Event History Analysis With Stata

Author: Hans-Peter Blossfeld,Katrin Golsch,Gotz Rohwer

Publisher: Psychology Press

ISBN: 1135595925

Category: Business & Economics

Page: 312

View: 6161

Event History Analysis With Stata provides an introduction to event history modeling techniques using Stata (version 9), a widely used statistical program that provides tools for data analysis. The book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models. The authors illustrate the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data, to data organization, to applications using the software, to the interpretation of results. The book also demonstrates, through example, how to implement hypotheses tests and how to choose the right model. The strengths and limitations of various techniques are emphasized in each example, along with an introduction to the model, details on how to input data, and the related Stata commands. Each application is accompanied by a brief explanation of the underlying statistical concept. Readers are offered the unique opportunity to easily run and modify all of the book’s application examples on a computer, by visiting the author’s Web site at Examples include survival rates of patients in medical studies; unemployment periods in economic studies; and the time it takes a criminal to break the law after his release in a criminological study. This new book supplements Event History Analysis, by Blossfeld et al, and Techniques of Event History Modeling, by Blossfeld and Rohwer, extending on their coverage of practical applications and statistical theory. Intended for researchers in a variety of fields such as statistics, economics, psychology, sociology, and political science, Event History Analysis With Stata also serves as a text, in combination with the authors’ other two books, for courses on event history analysis.

Lifetime Data: Models in Reliability and Survival Analysis

Author: Nicholas P. Jewell,Alan C. Kimber,Mei-Ling Ting Lee,G. Alex Whitmore

Publisher: Springer Science & Business Media

ISBN: 1475756542

Category: Mathematics

Page: 410

View: 2584

Statistical models and methods for lifetime and other time-to-event data are widely used in many fields, including medicine, the environmental sciences, actuarial science, engineering, economics, management, and the social sciences. For example, closely related statistical methods have been applied to the study of the incubation period of diseases such as AIDS, the remission time of cancers, life tables, the time-to-failure of engineering systems, employment duration, and the length of marriages. This volume contains a selection of papers based on the 1994 International Research Conference on Lifetime Data Models in Reliability and Survival Analysis, held at Harvard University. The conference brought together a varied group of researchers and practitioners to advance and promote statistical science in the many fields that deal with lifetime and other time-to-event-data. The volume illustrates the depth and diversity of the field. A few of the authors have published their conference presentations in the new journal Lifetime Data Analysis (Kluwer Academic Publishers).

Survival Analysis

Models and Applications

Author: Xian Liu

Publisher: John Wiley & Sons

ISBN: 1118307674

Category: Mathematics

Page: 427

View: 982

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.

Modelling Survival Data in Medical Research, Second Edition

Author: David Collett

Publisher: CRC Press

ISBN: 1584883251

Category: Mathematics

Page: 410

View: 6992

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.

Advances in Degradation Modeling

Applications to Reliability, Survival Analysis, and Finance

Author: M.S. Nikulin,Nikolaos Limnios,N. Balakrishnan,Waltraud Kahle,Catherine Huber-Carol

Publisher: Springer Science & Business Media

ISBN: 9780817649241

Category: Mathematics

Page: 416

View: 6347

This volume is a collection of invited chapters covering recent advances in accelerated life testing and degradation models. The book covers a wide range of applications to areas such as reliability, quality control, the health sciences, economics and finance. It is an excellent reference for researchers and practitioners in applied probability and statistics, industrial statistics, the health sciences, quality control, economics, and finance.

An Introduction to Survival Analysis Using Stata, Second Edition

Author: Mario Cleves

Publisher: Stata Press

ISBN: 1597180416

Category: Computers

Page: 372

View: 3330

An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject. This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata's new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression. After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata's survival analysis routines.

Forecasting the Health of Elderly Populations

Author: Kenneth G. Manton,Burton H. Singer,Richard M. Suzman

Publisher: Springer Science & Business Media

ISBN: 1461393329

Category: Mathematics

Page: 371

View: 2467

Models to forecast changes in mortality, morbidity, and disability in elderly populations are essential to national and state policies for health and welfare programs. This volume presents a wide-ranging survey of the forecasting of health of elderly populations, including the modelling of the incidence of chronic diseases in the elderly, the differing perspectives of actuarial and health care statistics, and an assessment of the impact of new technologies on the elderly population. Amongst the topics covered are - uncertainties in projections from census and social security data and actuarial approaches to forecasting - plausible ranges for population growth using biol ogical models and epidemiological time series data - the financing of long term care programs - the effects of major disabling diseases on health expenditures - forecasting cancer risks and risk factors As a result, this wide-ranging volume will become an indispensable reference for all those whose research touches on these topics.

Competing Risks and Multistate Models with R

Author: Jan Beyersmann,Arthur Allignol,Martin Schumacher

Publisher: Springer Science & Business Media

ISBN: 1461420350

Category: Mathematics

Page: 245

View: 9630

This book covers competing risks and multistate models, sometimes summarized as event history analysis. These models generalize the analysis of time to a single event (survival analysis) to analysing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models). Both R and multistate methods are promoted with a focus on nonparametric methods.

Analysing Survival Data from Clinical Trials and Observational Studies

Author: Ettore Marubini,Maria Grazia Valsecchi

Publisher: John Wiley & Sons

ISBN: 9780470093412

Category: Mathematics

Page: 414

View: 9898

A practical guide to methods of survival analysis for medical researchers with limited statistical experience. Methods and techniques described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and observational studies to describe methods of analysing and reporting results. Also reviews the features and performance of statistical software available for applying the methods of analysis discussed.

Survival Analysis

A Self-Learning Text, Third Edition

Author: David G. Kleinbaum,Mitchel Klein

Publisher: Springer

ISBN: 1441966463

Category: Medical

Page: 700

View: 1204

An excellent introduction for all those coming to the subject for the first time. New material has been added to the second edition and the original six chapters have been modified. The previous edition sold 9500 copies world wide since its release in 1996. Based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Provides a "user-friendly" layout and includes numerous illustrations and exercises. Written in such a way so as to enable readers learn directly without the assistance of a classroom instructor. Throughout, there is an emphasis on presenting each new topic backed by real examples of a survival analysis investigation, followed up with thorough analyses of real data sets.