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–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 a professor of sociology and director of the Institute for Family Research at Bamberg University. Former editor of the European Sociological Review, he received his PhD in economics from the University of Mannheim in 1984.
Katrin Golsch is an assistant professor of economics at the University of Cologne. She received her PhD in sociology from Bielefeld University in Germany in 2004 under the supervision of Hans-Peter Blossfeld. She teaches a course on introduction to Stata.
Götz Rohwer is a professor of social research and statistics at the Ruhr–University Bochum. He is the author of several books, including a monograph on probability in the health sciences.
Preface
1. INTRODUCTION
Causal Modeling and Observation Plans
Cross-Sectional Data
Panel Data
Event History Data
Event History Analysis and Causal Modeling
2. EVENT HISTORY DATA STRUCTURES
Basic Terminology
Event History Data Organization
3. NONPARAMETRIC DESCRIPTIVE METHODS
Life Table Method
Product-Limit Estimation
Comparing Survivor Functions
4. EXPONENTIAL TRANSITION RATE MODELS
The Basic Exponential Model
Maximum Likelihood Estimation
Models without Covariates
Time-Constant Covariates
Models with Multiple Decisions
Models with Multiple Episodes
5. PIECEWISE CONSTANT EXPONENTIAL MODELS
The Basic Model
Models without Covariates
Models with Proportional Covariate Effects
Models with Period-Specific Effects
6. EXPONENTIAL MODELS WITH TIME-DEPENDENT COVARIATES
Parallel and Interdependent Processes
Interdependent Processes: The System Approach
Interdependent Processes: The Causal Approach
Episode Splitting with Qualitative Covariates
Episode Splitting with Quantitative Covariates
Application Examples
7. PARAMETRIC MODELS OF TIME-DEPENDENCE
Interpretation of Time-Dependence
Gompertz Models
Weibull Models
Log-Logistic Models
Log-Normal Models
8. METHODS TO CHECK PARAMETRIC ASSUMPTIONS
Simple Graphical Methods
Pseudoresiduals
9. SEMIPARAMETRIC TRANSITION RATE MODELS
Partial Likelihood Estimation
Time-Dependent Covariates
The Proportionality Assumption
Baseline Results and Survivor Functions
Application Example
10. PROBLEMS OF MODEL SPECIFICATION
Unobserved Heterogeneity
Models with a Mixture Distribution
Models with a Gamma Mixture
Exponential Models with a Gamma Mixture
Weibull Models with a Gamma Mixture
Discussion
