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Re: st: correct for selection bias in survival analysis
From
Antoine Terracol <[email protected]>
To
[email protected]
Subject
Re: st: correct for selection bias in survival analysis
Date
Wed, 05 May 2010 13:06:55 +0200
Bonjour Marguerite,
as Maarten said earlier, including the IMR will not work in your setting
because the second stage is not linear, and also because the IMR rests
on the bivariate normality assumption, and your duration model has a
log-logistic distribution.
To my knowledge, there is no "simple" (as in "a couple of lines of
code") way to correct for selection biais in duration models in Stata.
You will need to write your own likelihood function, the simplest way to
do it would be to assume a log-normal distribution for the duration
model, and a Probit for the selection stage, taking advantage of the
bivariate normality of your error terms. For more general setups, you
may want to have a look at
Prieger, J. E. "A flexible parametric selection model for non-normal
data with application to health care usage" Journal of Applied
Econometrics, 2002, 17(4), 367-392
In addition, it seems you actually have a kind of double hurdle model
with two successive selection stages: having had a civil war and,
conditional on civil war, that peace was settled.
Given your research question, I assume you do not have many data points,
so you should try to stick to the simplest setup possible, and forget
about the double selection...
Antoine
PS: I have some old code for the log-normal duration model with
selection, feel free to send me an email privately if you're interested
in it (ou de passer à la MSE dans le courant de la semaine prochaine)
On 05/05/2010 12:44, Marguerite Duponchel wrote:
I am working on the duration of post civil war peace so I want to correct for
the fact that my sample is restricted to countries who 1/had a civil war, 2/
the peace was settled.
Thanks.
Quoting Maarten buis<[email protected]>:
--- On Wed, 5/5/10, Marguerite Duponchel wrote:
It's just that as I am the one creating the selection bias
on a full available sample, I thought there might be a way
to correct it while using the info in the complete data
which frailty would not integrate.
The easiest solution is not to make the selection or, if you
want to work with a smaller dataset, make the selection
random. Otherwise I don't understand why you would want to
make a selection and than "correct" for the fact that you
made a selection.
Hope this helps,
Maarten
--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://www.maartenbuis.nl
--------------------------
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