本书内容如下：

Preface
1 Introduction
1.1 Causal Modeling and Observation Plans
1.1.1 Cross-Sectional Data
1.1.2 Panel Data
1.1.3 Event History Data
1.2 Event History Analysis and Causal Modeling
2 Event History Data Structures
2.1 Basic Terminology
2.2 Event History Data Organization
3 Nonparametric Descriptive Methods
3.1 Life Table Method
3.2 Product-Limit Estimation
3.3 Comparing Survivor Functions
4 Exponential Transition Rate Models
4.1 The Basic Exponential Model
4.1.1 Maximum Likelihood Estimation
4.1.2 Models without Covariates
4.1.3 Time-Constant Covariates
4.2 Models with Multiple Decisions
4.3 Models with Multiple Episodes
5 Piecewise Constant Exponential Models
5.1 The Basic Model
5.2 Models without Covariates
5.3 Models with Proportional Covariate Effects
5.4 Models with Period-Specific Effects
6 Exponential Models with Time-Dependent Covariates
6.1 Parallel and Interdependent Processes
6.2 Interdependent Processes: The System Approach
6.3 Interdependent Processes: The Causal Approach
6.4 Episode Splitting with Qualitative Covariates
6.5 Episode Splitting with Quantitative Covariates
6.6 Application Examples

7 Parametric Models of Time-Dependence
7.1 Interpretation of Time-Dependence
7.2 Gompertz Models
7.3 Weibull Models
7.4 Log-Logistic Models
7.5 Log-Normal Models
8 Methods to Check Parametric Assumptions
8.1 Simple Graphical Methods
8.2 Pseudoresiduals
9 Semiparametric Transition Rate Models
9.1 Partial Likelihood Estimation
9.2 Time-Dependent Covariates
9.3 The Proportionality Assumption
9.4 Baseline Results and Survivor Functions
9.5 Application Example

10 Problems of Model Specification
10.1 Unobserved Heterogeneity
10.2 Models with a Mixture Distribution
10.2.1 Models with a Gamma Mixture
10.2.2 Exponential Models with a Gamma Mixture
10.2.3 Weibull Models with a Gamma Mixture
10.3 Discussion
References
About the Authors
Index