Scope

Event history analysis has become an important analytical tool in many fields of the social sciences. This course covers applied event history analysis. We will examine the standard tools used in the field-things like life tables, Kaplan Meier estimates, Cox proportional hazards model, and parametric survival models. Additionally, we will build a tool kit for developing custom 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, although many of the examples in this course are taken from demography.

The objectives of this course are (1) provide you with tools and concepts for solving quantitative problems involving the statistical analysis of time to events; (2) provide a took kit for developing custom event history models, (3) provide sufficient historical, intellectual, and mathematical background so that you can evaluate contemporary research using event history methods.

Times

The class is Tuesday and Thursdays, 1:30-3:20, 217 Denny Hall.

Office hours: I will usually be available after class for office hours. Other times can be arranged.

Readings

The textbooks are

1. Box-Steffensmeier JM, Jones BS (2004) Event History Modeling: A Guide for Social Scientists. Cambridge: Cambridge University Press.
2. Allison PD (1995) Survival Analysis Using the SAS System: A Practical Guide. Cary, NC: SAS Institute Inc.

Additional readings and handouts 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.

Grading

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.

Problem sets

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.

Software

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 http://mlelabs.com 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

Projects: 40% of your course grade will be based on a project. This project can take one of several forms:

1. a new research proposal in preliminary form incorporating one or more of the methods covered in the course
2. a completed existing research or funding proposal (a proposal started in another course, for example) that is revised to include one or more of the methods covered in this course
3. a new manuscript in which you have applied methods covered in this course;
4. a term paper in which a dataset has been analyzed using methods from this course
5. a completed poster presentation with original research, analysis, and presentation of an event history project

Topics and Schedule

Week 1: Introduction to Event History Analysis (Mar 31, Apr 2)

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 Mar 31

Overheads Apr 2

Week 2: Parametric Survival Models (Apr 7, Apr 9)

Reading: BSJ Ch 3; Allsion Ch 2; Distributions Handout; Likelihood Handout.

Overheads Apr 7

Overheads Apr 9

Problem set 1 distributed (Tuesday).

Week 3: More Parametric Survival Models (Apr 14, 16)

Reading: BSJ Ch 3; Allsion Ch 4; Messy Data Handout; Covariates Handout.

Overheads Apr 14

Overheads Apr 16

Problem set 1 due (Tuesday)

Problem set 2 distributed (Tuesday).

Week 4: Empirical and seemingly empirical models (Apr 21, 23)

Reading: Gehan (1969); Blossfeld and Rohwer Ch 3; Allsion Ch 3.

Overheads Apr 21

Overheads Apr 23

Problem set 2 due (Tuesday)

Problem set 3 distributed (Tuesday).

Week 5: Cox Proportional Hazards Models (Apr 28, 30)

Reading: BSJ Ch 4; Allsion Ch 5.

Overheads Apr 28

Problem set 3 due (Tuesday)

Overheads Apr 30

Problem set 4 distributed (Thursday).

Week 6: More Cox Model and Piecewise Models (May 5, 7)

Reading: BSJ Ch 4; Allson Ch 7.

Overheads May 5

Overheads May 7

Week 7: Model Selection and Diagnostics (May 12, 14)

Reading: BSJ Ch 6, 8; Wood et al. (1994).

Overheads May 12

Overheads May 14

Problem set 4 due (Tuesday)

Week 8: Models of Mixed Populations (Unobserved Heterogeneity I) (May 19, 21)

Reading: Holman (2004)

Overheads May 19

Overheads May 21

Problem set 5 distributed (Thursday Tuesday).

Week 9: Continuous Unobserved Heterogeneity (May 26, 28)

Reading: BSJ Ch 9; Vaupel and Yashin (1985).

Overheads May 26

Overheads May 28

Week 10: Some Advanced Models (Jun 2, 4)

Reading: BSJ Ch 10; Allsion Ch 6, 8; Wood et al. (1994); Strang and Tuma (1993)

Overheads Jun 2

Overheads Jun 4

Problem set 5 due (Tuesday)

Paper Due: Friday, Jun 12, by 2:30 pm

Other stuff

References