Re: st: Median survival times via Poisson model, using -margins- ?

[Austin Nichols <austinnichols@gmail.com>]

Andrew Lover <graupel75@gmail.com>:
Why not:

use http://www.pauldickman.com/survival/diet, clear
gen t=dox - doe
stset t
stci if hieng==1, by(job)

? Do your example data not represent what you actually have?

On Mon, Mar 25, 2013 at 10:55 PM,  <graupel75@gmail.com> wrote:
> Dear 'Listers,
>
> I have found multiple references to calculating median survival times
> for sub-populations from Poisson survival models (eg. Kelsey JK,
> Whittemore AS, Evans AS, Thompson WD. Methods in observational
> epidemiology. 2nd ed. New York: Oxford University Press, 1996, pgs.
> 31-3). A general scheme reported is :
>
> We computed mu as the unweighted average of these 18 death rates [Per
> categorical from Poisson model]. The risk of death at time t, R(t),
> was computed as R(t) = 1 - e ^-(mu*t), where t was in years. Median
> survival was computed by finding the value of t satisfying the
> relation, R(t) = 1 - e ^-(mu * t) = 0.5.
>
> I suspect *anything* is possible using -margins- but am stymied on how
> to get there with confidence intervals for the median times.
>
> A (not very interesting or well-modelled) example:
>
> ***** Start example *****
> use http://www.pauldickman.com/survival/diet, clear
>
> gen survdays = dox - doe
> gen survyear = survdays/365.25
> stset survyear, failure(chd == 1) id(id)
>
> stsplit, at(fail)
>
> collapse (sum) survyear chd, by(hieng job)
> sum surv, detail
>
> nbreg chd hieng i.job, exposure(survyear) irr
>
> // margins
>
> ***** End example *****
>
> Any thoughts on how to predict median survival times with 95% CIs for
> the three levels of 'job' at say, hieng ==1?
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