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.
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.
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.
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.
Furthermore, to obtain a parsimonious model and to improve interpretation of parameters therein, variable selection and estimation for both fixed and random effects are developed by penalized likelihood. We illustrate the method using simulation studies as well as a real data application from The Programme for the International Assessment of Adult Competencies (PIAAC). Chapter 3 concerns rare events and sparse covariates in event history analysis. In large-scale longitudinal observational databases, the majority of subjects may not experience a particular event of interest. Furthermore, the associated covariate processes could also be zero for most of the subjects at any time. We formulate such setting of rare events and sparse covariates under the proportional intensity model and establish the validity of using the partial likelihood estimator and the observed information matrix for inference under this framework.
Social scientists are interested in events and their causes. Although event histories are ideal for studying the causes of events, they typically possess two features—censoring and time-varying explanatory variables—that create major problems for standard statistical procedures. Several innovative approaches have been developed to accommodate these two peculiarities of event history data. This volume surveys these methods, concentrating on the approaches that are most useful to the social sciences. In particular, Paul D. Allison focuses on regression methods in which the occurrence of events is dependent on one or more explanatory variables. He gives attention to the statistical models that form the basis of event history analysis, and also to practical concerns such as data management, cost, and useful computer software. The Second Edition is part of SAGE’s Quantitative Applications in the Social Sciences (QASS) series, which continues to serve countless students, instructors, and researchers in learning the most cutting-edge quantitative techniques.
Serving as both a student textbook and a professional reference/handbook, this volume explores the statistical methods of examining time intervals between successive state transitions or events. Examples include: survival rates of patients in medical studies, unemployment periods in economic studies, or the period of time it takes a criminal to break the law after his release in a criminological study. 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 the specific questions of data organization, to the concrete application of available program packages and the interpretation of the obtained results. Event History Analysis: * makes didactically accessible the inclusion of covariates in semi-parametric and parametric regression models based upon concrete examples * presents the unabbreviated close relationship underlying statistical theory * details parameter-free methods of analysis of event-history data and the possibilities of their graphical presentation * discusses specific problems of multi-state and multi-episode models * introduces time-varying covariates and the question of unobserved population heterogeneity * demonstrates, through examples, how to implement hypotheses tests and how to choose the right model.
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 http://www.uni-bamberg.de/sowi/soziologie-i/eha/. 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.
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.
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.
A compendium of studies drawn from an international conference, this volume includes the newest and most substantive work on event history analysis. Researchers at four institutions convened, shared models of analysis, and collected their findings for the first time. The studies included represent work done in the following organizations: The Max Planck Institute in West Germany; The U.S. Social Science Research Council's Committee on Comparative Stratification; The Special Research Unit of the Deutsche Forschungsgemeinschaft; and MASO, and informal German working group on mathematical sociology. This book is one of twelve in the University of Wisconsin Press's Life Course Studies series, arranged by the series editors David L. Featherman and David I. Kertzer.
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.
This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage of the major approaches to analysis, while emphasizing the modeling assumptions that they are based on. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions. Parametric, nonparametric and semiparametric methodologies are all covered, with procedures for estimation, testing and model checking.
Author: Professor of Economics and Public Affairs Associate Director James Trussell
Publisher: Oxford University Press on Demand
Category: Social Science
Event history analysis--the study of individual life histories--has developed rapidly over the past few years. This volume illustrates the use of the new techniques at the frontier of the subject. The number of surveys undertaken throughout the world to collect detailed information on the timing of events in individual lives--such as fertility surveys or migration histories--has increased, and new methods to analyze such data have developed. Unresolved technical and practical issues remain, however, and researchers often have limited experience of the new techniques--this volume addresses these issues and provides information on the new methodologies. The book covers three main areas. First, it summarizes the work on the incorporation of unmeasured heterogeneity into the analysis of event histories; secondly, it introduces a series of 'competitions' in which pairs of teams are assigned to analyse the same topic using the same data; finally, it discusses other methodological issues such as the treatment of missing data, the analysis of current-status data, and the relation between discrete and continuous time models.
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.