1. Introduction
Problems in the analysis of event histories
An overview of event history methods
2. A discrete-time method
A discrete-time example
The discrete-time hazard rate
A logit regression model
Estimating the model
Estimates for the biochemistry example
The likelihood-ratio chi-square test
Problems with the discrete-time method
Discrete versus continuous time
3. Parametric methods for continuous-time data
The continuous-time hazard rate
Continuous-time regression models
Maximum likelihood estimation
An empirical example
Censoring
Some other models
Choosing a model
Unobserved sources of heterogeneity
4. Proportional hazards and partial likelihood
The proportional hazards model
Partial likelihood
Time-varying explanatory variables
Adequacy of the proportional hazards model
Choice of origin of the time scale
Partial likelihood for discrete-time data
5. Multiple kinds of events
A classification of multiple kinds of events
Estimation for multiple kinds of events
Models for competing risks
An empirical example of competing risks
Dependence among different kinds of events
6. Repeated events
A simple approach
Problems with repeated events
Extending the recidivism example
Left censoring
7. Change of states
Transition rates
An analysis of job changes
Simplifying the model
8. Conclusion.