Event History Analysis with R  book cover
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1st Edition

Event History Analysis with R




ISBN 9781439831649
Published April 3, 2012 by CRC Press
236 Pages 75 B/W Illustrations

 
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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.

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.

Table of Contents

Preface

Event History and Survival Data
Introduction
Survival Data
Right Censoring
Left Truncation
Time Scales
Event History Data
More Data Sets

Single Sample Data
Introduction
Continuous Time Model Descriptions
Discrete Time Models
Nonparametric Estimators
Doing it in R

Cox Regression
Introduction
Proportional Hazards
The Log-Rank Test
Proportional Hazards in Continuous Time
Estimation of the Baseline Hazard
Explanatory Variables
Interactions
Interpretation of Parameter Estimates
Proportional Hazards in Discrete Time
Model Selection
Male Mortality

Poisson Regression
Introduction
The Poisson Distribution
The Connection to Cox Regression
The Connection to the Piecewise Constant Hazards Model
Tabular Lifetime Data

More on Cox Regression
Introduction
Time-Varying Covariates
Communal covariates
Tied Event Times
Stratification
Sampling of Risk Sets
Residuals
Checking Model Assumptions
Fixed Study Period Survival
Left- or Right-Censored Data

Parametric Models
Introduction
Proportional Hazards Models
Accelerated Failure Time Models
Proportional Hazards or AFT Model?
Discrete Time Models

Multivariate Survival Models
Introduction
Frailty Models
Parametric Frailty Models
Stratification

Competing Risks Models
Introduction
Some Mathematics
Estimation
Meaningful Probabilities
Regression
R Code for Competing Risks

Causality and Matching
Introduction
Philosophical Aspects of Causality
Causal Inference
Aalen’s Additive Hazards Model
Dynamic Path Analysis
Matching
Conclusion

Basic Statistical Concepts
Introduction
Statistical Inference
Asymptotic theory
Model Selection

Survival Distributions
Introduction
Relevant Distributions in R
Parametric Proportional Hazards and Accelerated Failure Time Models

A Brief Introduction to R
R in General
Some Standard R Functions
Writing Functions
Graphics
Probability Functions
Help in R
Functions in eha and survival
Reading Data into R

Survival Packages in R
Introduction
eha
survival
Other Packages

Bibliography
Index

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Author(s)

Biography

Göran Broström is a professor emeritus of statistics in the Centre for Population Studies at Umeå University in Sweden.

Reviews

"This book in The R Series from Chapman & Hall acts much as a companion to the R package eha by the same author. … If one wants to analyse such data using R, then the book is well worthwhile. Although it is written more from the point of view of a reader comfortable in using R [and] wanting to learn more about demographic data, it also offers something for the demographer looking to extend the scope of their analyses. … the depth of treatment is about right to form the core of a lecture course …"
—Mark Bebbington, Australian & New Zealand Journal of Statistics, 2013