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From | graupel75@gmail.com |
To | Statalist <statalist@hsphsun2.harvard.edu> |
Subject | st: Median survival times via Poisson model, using -margins- ? |
Date | Tue, 26 Mar 2013 10:55:47 +0800 |
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? cheers- Andrew Lover ______________________________________________ Epidemiologist Centre for Infectious Disease Epidemiology Research (CIDER) Saw Swee Hock School of Public Health National University of Singapore * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/