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Full Description
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.
Contents
Contents: Foreword. 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. Listing of the FORTRAN Program PR3FUN Written by Trond Petersen. Listing of the FORTRAN Program for Episode Splitting Given Discrete Time-Dependent Covariates. Listing of the FORTRAN Program for Episode Splitting Given Continuous Time-Dependent Covariates. Listing of the GLIM Macros to Estimate the Weibull and Log-Logistic Models of Roger and Peacock.
