Causal Analysis with Event History Data Using Stata

1 Introduction 1

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

1.2.1 Causal Explanations

1.2.2 Transition Rate Models

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 Destinations

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 for Testing 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 Stratification with Covariates and for Multiepisode Data

9.5 Baseline Rates and Survivor Functions

9.6 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

10.2.4 Random Effects for Multiepisode Data

10.3 Discussion

11 Sequence Analysis

Brendan Halpin

11.1 What is Sequence Analysis?

11.1.1 Sequence Data

11.1.2 The Value of a Holistic View

11.2 Defining Distances

11.2.1 Hamming Distance

11.2.2 Optimal Matching Distance

11.2.3 Other Distances

11.2.4 Determining State Distances

11.3 Doing Sequence Analysis in Stata .

11.3.1 Example Data

11.3.2 A First Look at the Data

11.4 Unary Summaries

11.5 Intersequence Distance

11.6 What to Do with Sequence Distances?

11.7 Optimal Matching Distance

11.8 Special Topics

11.8.1 Other Distance Measures

11.8.2 Ideal Types

11.8.3 Multichannel Sequence Analysis

11.8.4 Dyadic Analysis

11.9 Conclusion

Appendix: Exercises

References

About the Authors