Event History Analysis with Stata

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. 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, software applications, and interpreting results.

After a substantive discussion of event history data, the authors explain, throughout the book, the theory and application of nonparametric methods, parametric regression, and finally the Cox model. The chapters on parametric analysis contain an instructive and detailed discussion on time-dependent covariates and practical situations where these might be present.

The book provides a link to the authors’ site, where readers can find the dataset and the code used in the examples. New in this edition is an Appendix that will allow readers to consolidate the concepts just learned by applying them in practical exercises using data from the German National Educational Panel Study.

Event History Analysis with Stata is aimed at the professional social scientist but could also serve as a graduate-level text.

Preface

 

1. INTRODUCTION

Causal Modeling and Observation Plans

Cross-Sectional Data
Panel Data
Event History Data

Event History Analysis and Causal Modeling

Causal explanations

Transition rate models

 

2. EVENT HISTORY DATA STRUCTURES

Basic Terminology
Event History Data Organization

Using event history data files with Stata

Executing Stata with a do-file

Single episode data

Multiepisode data

 

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

Conclusion: Estimating time-dependent models with Stata

 

8. METHODS TO CHECK PARAMETRIC ASSUMPTIONS

Simple Graphical Methods
Pseudoresiduals

 

9. SEMIPARAMETRIC TRANSITION RATE MODELS

Partial Likelihood Estimation
Time-Dependent Covariates
The Proportionality Assumption

Stratification with covariates and for multiepisode data
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

Random effects for multiepisode data

Discussion

 

11. INTRODUCTION TO SEQUENCE ANALYSIS BY BRENDAN HALPIN

Defining distances

Doing sequence analysis in Stata

Unary summaries

Intersequence distance

What to do with sequence distances?

Optimal matching distance

Special topics

Conclusion

Author: Hans-Peter Blossfeld, Götz Rohwer and Thorsten Schneider
Edition: Second Edition
ISBN-13: 978-1-1380-7085-1
©Copyright: 2019
Versione e-Book disponibile

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.