What is Event History Analysis?. Fiona Steele Centre for Multilevel Modelling University of Bristol. Overview. Example applications of EHA Event history data and possible sources Methods of analysis with application to timing of 1 st partnership Descriptive analysis
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
What is Event History Analysis?
Centre for Multilevel Modelling
University of Bristol
Methods for analysis of length of time until the occurrence of some event. The dependent variable is the duration until event occurrence.
Types of Event History Data
Key Quantities in EHA
Estimator of survivor function for interval t is
E.g. probability of surviving to the start of 3rd interval
= probability no event in 1st interval and no event in 2nd interval
Source: Subsample from the National Child Development Study
Introducing Covariates: Event History Modelling
There are many different types of event history model, which
vary according to:
The Cox Proportional Hazards Model
The most commonly applied model which:
hi(t) is hazard for individual i at time t
xi is a covariate with coefficient β
h0(t) is the baseline hazard, i.e. hazard when xi=0
The Cox model can be written
hi(t) = h0(t) exp(βxi)
or sometimes as
log hi(t) = log h0(t) + βxi
Note: x could be time-varying, i.e. xi(t)
The hazard of partnering at age t is 1.5 times higher for women
than for men.
So women partner at an earlier age than men.
We assume that the gender difference in the hazard is the same for
The response variable for a discrete-time model is the binary indicator of event occurrence yi(t).
The hazard function is the probability that yi(t)=1.
Fit a logistic regression model of the form:
category for t
Exp(B) are (multiplicative) effects on the odds of partnering at age t
Women partner more quickly than men.
Enrolment in full-time education is associated with a
(1-0.324)100=68% reduction in the odds of partnering, i.e. a delay
Proportional Gender Effects
Singer, J.D. and Willet, J.B. (1993) “It’s about time: Using discrete-time
survival analysis to study duration and the timing of events.” Journal
of Educational Statistics, 18: 155-195.
Blossfeld, H.-P. and Rohwer, G. (2007) Event History Analysis with Stata.
Mahwah (NJ): Lawrence Erlbaum.
Steele, F. (2005) Event History Analysis. NCRM Review Paper NCRM/004.
Downloadable from http://www.ncrm.ac.uk/publications/index.php.
Steele, F., Goldstein, H. and Browne, W. (2004) “A general multistate
competing risks model for event history data, with an application to a study of
contraceptive use dynamics.” Statistical Modelling, 4: 145-159.