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Re: st: dropping vars from analysis under conditions


From   Richard Williams <richardwilliams.ndu@gmail.com>
To   statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu
Subject   Re: st: dropping vars from analysis under conditions
Date   Tue, 17 Apr 2012 22:16:56 -0500

At 06:12 PM 4/17/2012, Steve Samuels wrote:
I think Maarten  is correct.  Katya is trying for a discrete duration
analysis, by adding the time intervals "interval2 interval3 interval4
interval5 interval6 interval7 ".  The logistic model operates
interval-by-interval.  Her event indicator is zero for all intervals
except those in which  an event occurred. Although the number of
observations is expanded, the number of events would not be; so the
effective amount of information in the data would be unchanged.


However I don't like Katya's analysis.  There's a lot I don't
understand, because she did not describe her data well or show us the
actual command.


 Among the issues:

 1) she doesn't include a cluster() option, so that standard errors
are probably incorrect; 2) the parameters of the logistic model are
not invariant to the choice of intervals; 3) the standard model would
be a discrete hazard or cumulative log-log model; 4) if she has survey
data, she is ignoring completely the sample design; 5) a discrete
hazard model without time-dependent covariates over a long number of
intervals is of doubtful use to me.

Paul Allison has written a couple of pieces about Discrete Time Methods for the Analysis of Event Histories. e.g. See his 1984 Green Sage Book on "Event History Analysis." I believe he shows the standard errors are correct and you don't need clustering. Being able to conveniently incorporate time-varying covariates is a big advantage of the approach. It also handles right-censoring well. I'm not sure about some of your other concerns, but I am guessing you could use the svy: prefix. My own example discussing this is at

http://www.nd.edu/~rwilliam/xsoc73994/Panel01-EHAX.pdf


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Richard Williams, Notre Dame Dept of Sociology
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