Event History Analysis with Stata, byHans-Peter Blossfeld, Götz Rohwer and Thorsten Schneider, presents survival analysis from a social science perspective. Introducing the mathematics and statistics of survival analysis, along with substantive discussions of social science data–specific issues, the authors give examples throughout using Stata (version 9) and data from the German Life History Study. The text covers both basic and advanced topics, from an introduction to life tables to fitting parametric models with unobserved heterogeneity. The authors aptly illustrate the entire research path required in applying event history analysis, from the initial problems of recording event-oriented data, to data organization, to software applications, to interpreting results.
Chapters 1 and 2 introduce event history data, discussed substantively, and the data structures used to contain them. Chapter 3 introduces nonparametric descriptive methods including life tables, product-limit estimation of the survivor function, and comparison of survivor functions.
Chapters 4–8 focus on estimation using parametric survival functions. This section discusses not the usual exponential, Weibull, etc., models but rather issues such as period-specific effects, qualitative and quantitative covariates, time-dependent covariates, and multiepisode data.
Chapter 9 discusses the Cox proportional hazards model, whereas chapter 10 covers problems with parametric model specification, including unobserved heterogeneity.
The book has a parametric model focus, which for some readers will be a strength and for others, a weakness. For the latter group, the weakness is minimal because the coverage of the Cox model is adequate given the foregoing discussion.
Event History Analysis with Stata is aimed at the professional social scientist but could also serve as a graduate-level text. A website providing supporting materials for the book, including the dataset files and do-files, is available at http://web.uni-bamberg.de/sowi/soziologie-i/eha/stata.
ABOUT THE AUTHORS
Hans-Peter Blossfeld is Professor of Sociology and director of the Institute for Family Research at Bamberg University.
Götz Rohwer is Professor of Social Research and Statistics at the Ruhr–University Bochum.
Thorsten Schneider is Professor of Sociology and the Dean of Studies in the Faculty of Social Sciences and Philosophy at the University of Leipzig.
1.1 Causal modeling and observation plans
1.1.2 Panel data
1.1.3 Event history data
1.2 Event history analysis and causal modeling
1.2.2 Transition rate models
2.2 Event history data organization
2.2.2 Executing Stata with a do-file
2.2.3 Single episode data
2.2.4 Multiepisode data
3.2 Product-limit estimation
3.3 Comparing survivor functions
4.1 The basic exponential model
4.1.2 Models without covariates
4.1.3 Time-constant covariates
4.2 Models with Multiple decisions
4.3 Models with multiple episodes
5.2 Models without covariates
5.3 Models with proportional covariate effects
5.4 Models with period-specific effects
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
7.2 Gompertz models
7.3 Weibull models
7.4 Log-logistic models
7.5 Log-normal models
7.6 Conclusion: Estimating time-dependent models with Stata
8.2 Pseudoresiduals
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
10.2 Models with a mixture distribution
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
11.2 Doing sequence analysis in Stata
11.3 Unary summaries
11.4 Intersequence distance
11.5 What to do with sequence distances?
11.6 Optimal matching distance
11.7 Special topics
11.8 Conclusion
