5 edition of Event history analysis found in the catalog.
Published
1989
by L. Erlbaum Associates in Hillsdale, N.J
.
Written in
Edition Notes
Includes bibliographical references (p. 288-294) and index.
| Statement | Hans-Peter Blossfeld, Alfred Hamerle, Karl Ulrich Mayer. |
| Contributions | Hamerle, Alfred, 1947-, Mayer, Karl Ulrich. |
| Classifications | |
|---|---|
| LC Classifications | H61 .B49 1989 |
| The Physical Object | |
| Pagination | 297 p. : |
| Number of Pages | 297 |
| ID Numbers | |
| Open Library | OL2031480M |
| ISBN 10 | 080580126X |
| LC Control Number | 88007073 |
Survival Analysis Survival analysis is also known as “event history analysis” (sociology), “duration models” (political science, economics), “hazard models” / “hazard rate models” (biostatistics, epi-demiology), and/or “failure-time models” (engineering, reliability analysis). • Commonality: Models for time-to-event data. Event history analysis is an umbrella term for a set of procedures for time series analysis. Event history models focus on the hazard function, which has to do with the probabilities that an event will occur after any given duration. Duration to the hazard of death was the classic example in medical research, but the hazard may have a positive.
Aim and Structure of the Book. Domains and Rationale for the Application of Event History Analysis. The Statistical Theory of Event History Analysis. Data Organization and Descriptive Methods. Semi-Parametric Regression Models: The Cox Proportional Hazards Model. Parametric Regression Models. Appendices: List of Variable Names Used in Examples. Introducing covariates: Event history modelling There are many di erent types of event history model, which vary according to: Assumptions about the shape of the hazard function Whether time is treated as continuous or discrete Whether the e ects of covariates can be assumed constant over time (proportional hazards) 22/File Size: 1MB.
5 Using Logit/Logistic regression models for discrete-time event history analysis Common in the “early” texts on Event History analysis (eg., Allison) Some key concepts carry over to continuous time models, though. We model periods of time during which respondents are “at risk” Example: Study over a six-year period, professors getting Size: KB. eha: Event History Analysis. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models.
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The book also explores such significant topics as missing data, hazard rate, Cox′s partial likelihood model, survivor function, and discrete-time logit by: 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 s: 1. 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 Cited by: Event History and Survival Analysis; A Comparative Study of the Individual and Contextual Determinants of Invalid Votes in Europe; A Quasi-Experimental Study: Using Mythbusters to Understand Research in Psychology; An Introduction to Experiments in Consumer Behavior; An Introduction to Generalized Linear Models.
Event History Analysis: Regression for Longitudinal Event Data, Issue 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/5(2).
Event History Analysis with Stata is an invaluable resource for both novice students and researchers who need an introductory textbook and experienced researchers (from sociology, economics, political science, pedagogy, psychology, or demography) who are looking for a practical handbook for their : Hans-Peter Blossfeld, Gotz Rohwer, Thorsten Schneider.
Event history analysis is a term commonly used to describe a variety of statistical methods that are designed to describe, explain or predict the occurrence of events. Outside the social sciences, these methods are often called survival analysis, owing to the fact that they were originally developed by biostatisticians to analyze the occurrence of deaths.
Book Description. 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.
This book can be read with a BUKU subscription. You get unlimited access to the entire library, with a BUKU subscription. Available in: Create free account. Details.
ISBN. Author(s) Melinda Mills. Publisher. SAGE Publications. Introducing Survival and Event History Analysis. Introducing Survival and Event History Analysis is an accessible, practical and comprehensive guide for researchers from multipl.
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 : Hardcover.
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 by: The analysis is performed on data that are exceptionally good for both network and event-history analysis: they include the whole population of.
Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems.
This topic is called reliability theory or reliability analysis in engineering, duration analysis or duration modelling in. Event History Analysis: Regression for Longitudinal Event Data.
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/5.
An introduction to survival and event history analysis. Pages Stochastic processes in event history analysis. Pages 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. I am collecting Life History Calendar data at the monthly level and trying to do something other than count data to recognize the most common sequences of events.
The event history analysis, from. As Event History Analysis with Stata (Blossfeld, Golsch, and Rohwer ) is basi-cally a Stata “translation” of the TDA-based Techniques of Event History Modeling (Blossfeld and Rohwer), it automatically inherits all the strengths of the latter.
Above all, it is the book’s general didactical concept that makes it a convinc-File Size: KB. event history analysis is used primarily in social science applications where events may be repeatable and an individual’s history of events is of interest.
The collection and management of event history data Event histories are collected in a number of social surveys. This books provides a concise and clear introduction to survival and event history analysis, including descriptive non-parametric methods, Cox proportional hazards, parametric models and model assessment.
It also covers models for frailty and recurrent events, discrete-time models and competing risks and multi-state by:. 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.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.Introducing Survival Analysis and Event History Analysis is an accessible, practical and comprehensive guide for researchers and students who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities.
