Event History Analysis with R
By Göran Broström
Published April 3rd 2012 by CRC Press – 236 pages
Series: Chapman & Hall/CRC The R Series
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.
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
Author Bio
Göran Broström is a professor of statistics at Umeå University in Sweden.
Name: Event History Analysis with R (Hardback) – CRC Press
Description: By Göran Broström. 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...
Categories: Quantitative Methods, Statistics for the Biological Sciences, Statistics & Computing, Regression Analysis and Multivariate Statistics
