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RE: st: Simulating failure times using discrete-time event history analysis

From   "Wallace, Geoffrey P" <>
To   "" <>
Subject   RE: st: Simulating failure times using discrete-time event history analysis
Date   Wed, 7 Jul 2010 15:22:06 -0400

Hi Maarten,

Thanks for the suggestions. Some of the previous work I'm looking to build on used a discrete-time approach, so I was trying to figure this out within that setting.

Cheers, Geoff

-----Original Message-----
From: [] On Behalf Of Maarten buis
Sent: Wednesday, July 07, 2010 11:50 AM
Subject: Re: st: Simulating failure times using discrete-time event history analysis

--- On Wed, 7/7/10, Wallace, Geoffrey P wrote:
> I am conducting an event history analysis using
> discrete-time data looking at the decision to ratify a
> number of international human rights treaties. The unit of
> analysis is the country-year. The main specification for the
> models is as follows, which uses cubic splines and a count
> variable to capture duration dependence.
> . logit treatyvar indepvars timecount spline1 spline2
> spline3, vce(cluster country)
> To explore substantive effects I've used the Clarify ado
> package to estimate the predicted probability of failure
> (ratify the treaty) based on different values for the
> explanatory variables.
> Rather than just the probability of failure, what I want to
> do is estimate the probable length of time it would take to
> fail based on setting different values for the explanatory
> variables, measured by the number of years, though still
> allowing for a fraction of a year (e.g. 2.5 years). I think
> it makes most sense to specify the length of time until
> failure as occurring once Probability(failure) is greater
> than 0.5, but I'm having trouble figuring out how to do this
> in Stata.

I know that ratification dates for environmental and ILO 
treaties are known up to the day (I used that for my master
thesis). I guess that the same is true for human rights 
treaties. Daily data is fine enough detail for me to regard 
this as continuous time, which saved me a lot of trouble /
additional assumptions. One advantage is that if you use
fully parametric (see: -help streg-) you can easily derive /
predict the median survival time, which seems to be what you
are after. Alternatively you should be able to do the same 
with the semi-parametric alternative -stpm2- (see -findit stpm2-). However, this would be harder for Cox regression (see:
-help stcox-) as that model delibarately does not model the 
baseline hazard function.

Hope this helps,

Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen


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