Event History Analysis with Stata

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.



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 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

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

2.2.1 Using event history data files with Stata
2.2.2 Executing Stata with a do-file
2.2.3 Single episode data
2.2.4 Multiepisode data


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
7.6 Conclusion: Estimating time-dependent models with Stata


8 Methods to check parametric assumptions
8.1 Simple graphic 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 Introduction to sequence analysis by Brendan Halpin
11.1 Defining distances
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


Appendix: exercises
About the Authors
Author: Hans-Peter Blossfeld, Katrin Golsch, Götz Rohwer
Edition: Second Edition
ISBN-13: 978-1-1380-7085-1
©Copyright: 2019 Routledge

Event History Analysis with Stata, by Hans-Peter Blossfeld, Katrin Golsch, and Götz Rohwer, presents survival analysis from a social science perspective. Introducing the mathematics and statistics of survival analysis, along with substantive discussions of social science data issues, the authors give examples throughout using Stata (version 15) and data from the German Life History Study.