Event History Analysis CS&SS 544
Office: Department of Anthropology, Denny 235, Box 353100
Scope: Event history analysis is 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, modeling an "immune" subpopulation, 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.
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.
Office hours: I will typically be available after class for office hours. Other times can be arranged. Call (3-7586) or email me with questions or to set up an appointment.
The textbooks are
- Box-Steffensmeier JM, Jones BS (2004) Event History Modeling: A Guide for Social Scientists. Cambridge: Cambridge University Press.
- Allison PD (2010) Survival Analysis Using the SAS System: A Practical Guide. Cary, NC: SAS Institute Inc. (Note: the 2010 edition is preferred, but the 1995 edition can be used).
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 research poster (40%). There are no exams.
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 are reduced 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 and works particularly well for this course. If there is sufficient interest, I will offer optional weekly computer lab sessions to help you work on course material using the mle programming language. There are a number of short courses and online tutorials that introduce R.
The mle package is freely available from my web page (http://faculty.washington.edu/djholman/mle) and works on most Windows, Macintosh or Linux computers. 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 develop skills in any other statistical programming language.
40% of your course grade will be based on a project in the form of a completed poster with original research, analysis, and presentation using the methods covered in this course. You will present your poster at a poster session during finals week. The poster session will be a joint event with one or two other CSSS courses, and will be widely advertised to faculty and graduate students. A one paragraph description of your project will be due during week 5. If you don't already have a data set in mind, start early locating one. The CSSCR data consultant (Tina Tian) can help identify and procure a relevant data set.
- Box-Steffensmeier and Jones Ch 1, 2
- Allison Ch 1
- Lecture 1 Notes
- Box-Steffensmeier and Jones Ch 3
- Allison Ch 2
- Review probability theory, distributions and random variables, and likelihood as needed: here
- Introduction to censoring and truncation handout
- Distributions Handout
- Likelihood Handout
- Gehan (1969)
- Blossfeld and Rohwer Ch 3
- Allison Ch 3
- Box-Steffensmeier and Jones Ch 4
- Allison Ch 5
- Notes on Writing an Event History Analysis Paper (Tuma)
- Box-Steffensmeier and Jones Ch 4
- Allson Ch 7
- Box-Steffensmeier and Jones Ch 6, 8
- Wood et al. (1994).
- Box-Steffensmeier and Jones Ch 9
- Vaupel and Yashin (1985).
- Date and time: Tuesday, Mar 17, 2:30pm-4:20pm, Hub 334