Event history analysis has become an important analytical tool in many fields of the social sciences. This course covers the standard tools used for event history analysis-things like parametric survival models, life tables, Kaplan Meier estimates, and the Cox proportional hazards model. Additionally, the course focuses on building your tool kit, so that you can develop custom event history models that involve non-standard methods like subgroup heterogeneity, incorporation of "immune" individuals, mixture models, models for clustered observations, multi-state models and social diffusion models.
This course is not specific to any field within the social sciences, but many of the examples are taken from demography.
Objectives: After completing this course you will have (1) a working familiarity with the tools and concepts for solving quantitative problems in the statistical analysis of time to events; (2) developed the skills and background to evaluate the use (and misuse) of event history analysis in contemporary social science research; (3) built a tool kit for developing custom event history models.
The class is 10:30 am-12:20 pm in
Mechanical Engineering (MEB) 401 Savery.
Office hours: I will usually be available after class for office hours. Other times can be arranged.
The textbooks are
Additional readings (here) and handouts (here) will supplement the text. These readings will illustrate principles discussed in lecture and the text, and will also be used as the basis for some class discussions.
A selection of readings (largely collected by past students of this course) are available here
Grades: There will be 5 problem sets (12% each) that will make up 60% of your final grade, and a final project (40%). There are no exams for this course.
The five problem sets will consist of analytical exercises and other short problems. Frequently, the problems will require the use of computer software.
I recommend that you get an account on the CSDE Windows network. The CSDE systems have many useful programs for doing event history analysis (request a Windows account here). Data sets for this course will be available on both the course web site and the CSDE server.
You can use books, readings, notes, and web pages to help you work on the problems. In fact, you can work in groups on most exercises. Grades for late problem sets will depreciate by 10% per day, including any fraction of a day late.
You can use any software that works for you and gets the job done. For example, when we work with the Cox proportional hazards regression model, almost any standard statistical software will work. For other assignments only a few "packages" will be able to easily perform the analysis. One option, and one I encourage, is that you begin working with a statistical programming language. Perhaps the best overall statistical programming language is R. However, the language mle written by your instructor is a good choice as well for advanced modeling. If there is sufficient interest, I will offer optional weekly sessions in a computer lab that introduces mle programming. There are a number of short courses and online tutorials that introduce R (or S-plus).
The mle package is freely available from here for use on your Windows or Linux computer. Extensive documentation is available online. You can download a pdf version of the documents for browsing or printing. The mle program is also installed on the CSDE terminal servers. Most of the exercises that you can do in mle can also be done in other statistical programming languages (S-plus, R, Matlab, Gauss, Octave). You are free to use any of these for your work under the idea that learning one such language will help you understand any other.
Projects: 40% of your course grade will be based on a project. This project can take one of several forms:
Week 1: Introduction to Event History Analysis (Jan 8, 10)
Reading: Box-Steffensmeier and Jones (BSJ) Ch 1, 2; Allison Ch 1; Lecture 1 Notes; Notes on Writing an Event History Analysis Paper (Tuma). Overheads Jan 8 Overheads Jan 10
Week 2: Parametric Survival Models (Jan 15, 17)
Reading: BSJ Ch 3; Allsion Ch 2; Distributions Handout; Likelihood Handout. Overheads Jan 15 Overheads Jan 17 Problem set 1 distributed (Tuesday).
Week 3: More Parametric Survival Models (Jan 22, 24)
Reading: BSJ Ch 3; Allsion Ch 4; Messy Data Handout; Covariates Handout. Overheads Jan 22 Overheads Jan 24 Problem set 1 due (Tuesday) Problem set 2 distributed (Tuesday).
Week 4: Empirical and seemingly empirical models (Jan 29, 31)
Reading: Gehan (1969); Blossfeld and Rohwer Ch 3; Allsion Ch 3. Overheads Jan 29 Overheads Jan 31 Problem set 2 due (Tuesday) Problem set 3 distributed (Tuesday).
Week 5: Cox Proportional Hazards Models (Feb 5, 7)
Reading: BSJ Ch 4; Allsion Ch 5. Overheads Feb 5 Overheads Feb 7 Problem set 3 due (Tuesday) Problem set 4 distributed (Tuesday). One paragraph project description due (Thursday)
Week 6: More Cox Model and Piecewise Models (Feb 12, 14)
Reading: BSJ Ch 4; Allson Ch 7. Overheads Feb 12 Overheads Feb 14
Week 7: Model Selection and Diagnostics (Feb 19, 21)
Reading: BSJ Ch 6, 8; Wood et al. (1994). Overheads Feb 19 Overheads Feb 21
Week 8: Models of Mixed Populations (Unobserved Heterogeneity I) (Feb 26, 28)
Reading: Holman (2004) Overheads Feb 26 Overheads Feb 28 Problem set 4 due (Tuesday) Problem set 5 distributed (Tuesday).
Week 9: Continuous Unobserved Heterogeneity (Mar 5, 7)
Reading: BSJ Ch 9; Vaupel and Yashin (1985). Overheads Mar 5 Overheads Mar 7
Week 10: Some Advanced Models (Mar 12, 14)
Reading: BSJ Ch 10; Allsion Ch 6, 8; Wood et al. (1994); Strang and Tuma (1993) Overheads Mar 12 Overheads Mar 14 Problem set 5 due (Thursday)
Paper Due: Monday, Mar 18, by 10:30 am in my Mailbox