Event History Analysis CS&SS 544 CSSS UW Seal

Event History Analysis CS&SS 544 (Winter 2014)

Darryl J. Holman
235 Denny Hall

Week 1 | Week 2 | Week 3 | Week 4 | Week 5 | Week 6 | Week 7 | Week 8 | Week 9 | Week 10 | Final


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.


Tuesday and Thursday at 10:30 am-12:20 pm in Savery (SAV) 138.

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

  1. Box-Steffensmeier JM, Jones BS (2004) Event History Modeling: A Guide for Social Scientists. Cambridge: Cambridge University Press.
  2. Allison PD (2010) Survival Analysis Using the SAS System: A Practical Guide. Cary, NC: SAS Institute Inc. (Note: either the 1995 or the 2010 edition may 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.

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 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. CSSCR has a data consultant who can help identify and procure relevant data sets.

Topics and Schedule

Week 1 | Week 2 | Week 3 | Week 4 | Week 5 | Week 6 | Week 7 | Week 8 | Week 9 | Week 10 | Final

Week 1: Introduction to Event History Analysis (Jan 7, 9)

  • 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 7
  • Overheads Jan 9

  • Week 2: Parametric Survival Models (Jan 14, 16)

  • Reading: BSJ Ch 3; Allsion Ch 2; Distributions Handout; Likelihood Handout.
  • Overheads Jan 14
  • Overheads Jan 16
  • Problem set 1 distributed (Tuesday).

  • Week 3: More Parametric Survival Models (Jan 21, 23)

  • Reading: BSJ Ch 3; Allsion Ch 4; Messy Data Handout; Covariates Handout.
  • Overheads Jan 21
  • Overheads Jan 23
  • Problem set 1 due (Tuesday).
  • Problem set 2 distributed (Tuesday).

  • Week 4: Empirical and seemingly empirical models (Jan 28, 30)

  • Reading: Gehan (1969); Blossfeld and Rohwer Ch 3; Allsion Ch 3.
  • Overheads Jan 28
  • Overheads Jan 30
  • Problem set 2 due (Tuesday).
  • Problem set 3 distributed (Tuesday).

  • Week 5: Cox Proportional Hazards Models (Feb 4, 6)

  • Reading: BSJ Ch 4; Allsion Ch 5.
  • Overheads Feb 4
  • Overheads Feb 6
  • Problem set 3 due (Tuesday).
  • One paragraph project description due (Thursday)

  • Week 6: More Cox Model and Piecewise Models (Feb 11, 13)

  • Reading: BSJ Ch 4; Allson Ch 7.
  • Overheads Feb 11
  • Overheads Feb 13
  • Problem set 4 distributed (Thursday).

  • Week 7: Model Selection and Diagnostics (Feb 18, 20)

  • Reading: BSJ Ch 6, 8; Wood et al. (1994).
  • Overheads Feb 18
  • Overheads Feb 20
  • Problem set 4 due (Thursday).

  • Week 8: Models of Mixed Populations (Unobserved Heterogeneity I) (Feb 25, 27)

  • Reading: Holman (2004)
  • Overheads Feb 25
  • Overheads Feb 27
  • Problem set 5 distributed (Tuesday).

  • Week 9: Continuous Unobserved Heterogeneity (Mar 4, 6)

  • Reading: BSJ Ch 9; Vaupel and Yashin (1985).
  • Overheads Mar 4
  • Overheads Mar 6

  • Week 10: Some Advanced Models (Mar 11, 13)

  • Reading: BSJ Ch 10; Allsion Ch 6, 8; Wood et al. (1994); Strang and Tuma (1993)
  • Overheads Mar 11
  • Overheads Mar 13
  • Problem set 5 due (Tuesday).

  • Poster Session: TBD, but during the week of March 17th.

    Other stuff

  • Full course syllabus
  • Handouts
  • Readings
  • Overheads
  • Homework Assignments
  • Selected (optional) Papers
  • mle Programming Language
  • References

  • Aalen OO, Borgan Ų, Gjessing HK (2008) Survival and Event History Analysis: A Process Point of View. New York: Springer.
  • **Allison PD (1984) Event history analysis: Regression for longitudinal event data. Newbury Park, CA: Sage Publications.
  • ***Allison PD (1995) Survival Analysis Using the SAS System: A Practical Guide. Cary, NC: SAS Institute Inc.
  • *Blossfeld H-P, Golsch K, Rohwer G (2007) Event History Analysis with Stata. Mahwah, NJ: Lawrence Erlbaum.
  • Blossfeld H-P, Hamerle A, Mayer KU (1989). Event history analysis. Hillsdale, New Jersey: Lawrence Erlbaum.
  • Blossfeld H-P, Rohwer G (1995). Techniques of Event History Modeling. Mahwah, NJ: Lawrence Erlbaum.
  • ***Box-Steffensmeier JM, Jones BS (2004) Event History Modeling: A Guide for Social Scientists. Cambridge: Cambridge University Press.
  • Cox DR, Oakes D (1984) Analysis of Survival Data. London: Chapman and Hall.
  • §Edwards AWF (1972) Likelihood. Cambridge: Cambridge University Press.
  • **Elandt-Johnson RC, Johnson NL (1980) Survival Models and Data Analysis. New York: John Wiley and Sons.
  • §Evans M, Hastings N, Peacock B (2000) Statistical Distributions. Third edition. New York: John Wiley and Sons.
  • Gehan EA (1969) Estimating survival functions from the life table. Journal of Chronic Diseases 13:629-644.
  • Holman DJ (2003) mle: A programming language for building likelihood models. Version 2.1. Volume 1. User's Manual. and Volume 2, Reference Manual. http://faculty.washington.edu/~djholman/mle.
  • Holman DJ (2003) Unobserved heterogeneity. In: Lewis-Beck MS, Bryman A, Liao TF, (eds.) Encyclopedia of Social Science Research Methods. Thousand Oaks, CA: Sage Publications
  • Hosmer DW, Lemeshow S (1999) Applied Survival Analysis: Regression Modeling of Time to Event Data. New York: John Wiley and Sons.
  • Kalbfleisch JD, Prentice RL (1980) The Statistical Analysis of Failure Time Data. New York: John Wiley & Sons.
  • *Klein JP, Moeschberger ML (1997) Survival Analysis: Techniques for Censored and Truncated Data. New York: Springer-Verlag.
  • London D (1997) Survival Models. Winsted, CT:ACTEX Publicatons.
  • Mayer KU, Tuma NB (eds.) (1990) Event History Analysis in Life Course Research. Madison:Univ. Wisconsin Press.
  • §Namboodiri K, Suchindran CM (1987) Life Table Techniques and their Applications. Orlando: Academic Press.
  • Nelson W (1982) Applied Life Data Analysis. New York: John Wiley and Sons.
  • Schoen R (1988). Modeling Multigroup Populations. New York: Plenum Press.
  • Singer JD, Willett JB (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence Oxford: Oxford University Press.
  • Strang D, Tuma NB (1993) Spatial and Temporal Heterogeneity in Diffusion, American Journal of Sociology 99(3):614-639.
  • Trussell J, Hankinson R, Tilton J (eds.) (1992) Demographic Applications of Event Histroy Analysis. Oxford:Clarendon Press
  • Tuma NB, Hannan MT (1984) Social Dynamics: Models and Methods. New York: Academic Press.
  • Vaupel JW, Yashin AI (1985) Heterogeneity's ruses: Some surprising effects of selection on population dynamics. American Statistician 39:176-85.
  • Vermunt JK (1997) Log-linear models for event histories. London: Sage Publications.
  • *Lee ET (1992) Statistical Methods for Survival Data Analysis (2nd edition). New York: John Wiley & Sons.
  • Wood JW, Holman DJ, Yashin A, Peterson RJ, Weinstein M, Chang M-c (1994) A multistate model of fecundability and sterility. Demography 31(3):403-426.
  • *Yamaguchi K (1991) Event History Analysis. Newbury Park, CA: Sage Publications, Inc.
  • * Recommended book for you collection
  • ** Recommended book for this course (optional)
  • *** Required book for this course
  • § Good technical reference.