Introducing Survival and Event History Analysis
- ISBN-10
- 1848601026
- ISBN-13
- 9781848601024
- Category
- Social Science
- Pages
- 279
- Language
- English
- Published
- 2011-01-19
- Publisher
- SAGE Publications
- Author
- Melinda Mills
Description
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.
Get the book
Other editions
Similar books
-
Survival and Event History Analysis: A Process Point of View
By Ornulf Borgan, Odd Aalen, Hakon GjessingThe 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.
-
Event History Analysis: Regression for Longitudinal Event Data
By Paul David AllisonAllison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem.
-
Event History Analysis with R
By Göran BroströmFeatures • Introduction to survival and event history analysis and how to solve problems with incomplete data using Cox regression. • Parametric proportional hazards models, including the Weibull, Exponential, Extreme Value, and ...
-
Survival and Event History Analysis
By Niels Keiding, PER KRAGH ANDERSENThis 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.
-
An Introduction to Survival Analysis Using Stata, Second Edition
By Mario ClevesThis book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ..."--Book jacket.
-
Event History Modeling: A Guide for Social Scientists
By Janet M. Box-Steffensmeier, Bradford S. JonesPublisher Description
-
Event History Analysis With Stata: 2nd Edition
By Hans-Peter Blossfeld, Gotz Rohwer, Thorsten SchneiderSinger, J. D., & Willett, J. B. (1991). Modeling the days of our lives: Using survival analysis when designing and analyzing longitudinal studies of duration and the timing of events. Psychological Bulletin, 110, 268–290.
-
Survival Analysis: A Self-Learning Text
By David G. Kleinbaum, Mitchel KleinA "user-friendly" layout includes numerous illustrations and exercises and the book is written in such a way so as to enable readers learn directly without the assistance of a classroom instructor.
-
Modeling Survival Data: Extending the Cox Model
By Terry M. Therneau, Patricia M. GrambschUsing the same likelihood expansion method [(A + y)t – 2]” ARE(Gompertz) = ARE(exp){1+++) 1 – c – log((A + y)t)]” ARE(Weibull) = ARE(exp){1 + on-e-Isermor, - where c denotes Euler's constant 0.577215. ... As with the exponential model, ...
-
Event History Analysis With Stata
By Hans-Peter Blossfeld, Gotz Rohwer, Katrin GolschThe book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models. T