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Author : Janet M. Box-Steffensmeier
Publisher : Cambridge University Press
Page : 236 pages
File Size : 16,19 MB
Release : 2004-03-29
Category : Political Science
ISBN : 9780521546737
Publisher Description
Author : Hans-Peter Blossfeld
Publisher : Psychology Press
Page : 321 pages
File Size : 42,9 MB
Release : 2001-09-01
Category : Psychology
ISBN : 1135639124
Including new developments and publications which have appeared since the publication of the first edition in 1995, this second edition: *gives a comprehensive introductory account of event history modeling techniques and their use in applied research in economics and the social sciences; *demonstrates that event history modeling is a major step forward in causal analysis. To do so the authors show that event history models employ the time-path of changes in states and relate changes in causal variables in the past to changes in discrete outcomes in the future; and *introduces the reader to the computer program Transition Data Analysis (TDA). This software estimates the sort of models most frequently used with longitudinal data, in particular, discrete-time and continuous-time event history data. Techniques of Event History Modeling can serve as a student textbook in the fields of statistics, economics, the social sciences, psychology, and the political sciences. It can also be used as a reference for scientists in all fields of research.
Author : Göran Broström
Publisher : CRC Press
Page : 238 pages
File Size : 16,33 MB
Release : 2018-09-03
Category : Mathematics
ISBN : 1315360527
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.
Author : Paul David Allison
Publisher : SAGE
Page : 92 pages
File Size : 13,5 MB
Release : 1984-11
Category : Social Science
ISBN : 9780803920552
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.
Author : Melinda Mills
Publisher : SAGE Publications
Page : 301 pages
File Size : 14,12 MB
Release : 2011-01-19
Category : Social Science
ISBN : 1848601026
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.
Author : Odd Aalen
Publisher : Springer Science & Business Media
Page : 550 pages
File Size : 26,27 MB
Release : 2008-09-16
Category : Mathematics
ISBN : 038768560X
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.
Author : David W. Hosmer, Jr.
Publisher : John Wiley & Sons
Page : 285 pages
File Size : 33,94 MB
Release : 2011-09-23
Category : Mathematics
ISBN : 1118211588
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.
Author : Jean-Raymond Abrial
Publisher : Cambridge University Press
Page : 613 pages
File Size : 13,37 MB
Release : 2010-05-13
Category : Computers
ISBN : 0521895561
A practical introduction to this model-based formal method, containing a broad range of illustrative examples.
Author : Gerhard Tutz
Publisher : Springer
Page : 252 pages
File Size : 28,82 MB
Release : 2016-06-14
Category : Mathematics
ISBN : 3319281585
This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.
Author : Luc Duchateau
Publisher : Springer Science & Business Media
Page : 329 pages
File Size : 43,34 MB
Release : 2007-10-23
Category : Mathematics
ISBN : 038772835X
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.