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Re: st: Compare observed - expected survival functions
Thanks to Enzo Coviello for the detailed response
--- Enzo Coviello <email@example.com> wrote:
> At 09.35 07/05/04 -0700, you wrote:
> >Dear statalisters,
> >Is it possible to evaluate the fit of a survival
> >by comparing the predicted survival function with
> >observed (Kaplan-Meier estimate). I think this
> >n was asked long time ago but I found no answers in
> >the archives. I also think that this kind of test
> >refered as "one-sample logrank test" but I am not
> >sure. I am asking this on behalf of a coleague of
> >but I am also curious.
> >Any help, thoughts or pointers to relevant
> >will be much appreciated.
> Hi Nikos,
> I found that a very good paper on this subject is
> the Tech. Report 63 that
> you can download from the site of prof. T. Therneau.
> Here Therneau says that one-sample log rank is
> equivalent to the test for
> intercept = 0 in a Poisson model where the offset
> term is log(n_expected).
> So typing
> - poisson depvar, exposure(n_expected)
> should yield the result you are looking for.
> Actually I believe that, to this aim, even the
> simpler strate with
> smr(varname) option should be equivalent.
> Formulas in this paper to estimate the expected
> survival function based on
> reference rates are implemented in -stexpect- a new
> Stata macro that you
> can download from SSC archive. Type findit stexpect
> or ssc desc stexpect
> for more details.
> I know that prof. Dickman also developed a new ado
> to estimate expected
> survival based on probability of death and applying
> a life table approach.
> Paul Dickman also wrote a wonderful paper on Stat.
> in Med. 2004 23: 51-64
> where he presents different regression models for
> relative survival, all of
> them can be estimated using Stata. My preferred
> method is achievable using
> the glm command with a new link function specified
> in the rs.ado.
> Briefly, a nice model to compare observed to
> expected survival could be:
> glm depvar indepvars, f(poisson) l(rs n_expected)
> rs.ado and more details can be found at Paul Dickman
> site www.paul.dickman.com.
> Hope this could be useful.
> Enzo Coviello
> Dipartimento di Prevenzione ASL BA/1
> via L. Barbera 27
> 70055 MINERVINO MURGE (BA)
> tel - fax +390883691053
> (home) +390883695055
> * For searches and help try:
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
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