Re: st: How to estimate adjusted survival curves after fitting Cox model

[Steve Samuels <sjsamuels@gmail.com>]

I'm not sure what you mean by "adjusted". The following code will
produce predicted survival curves for different combinations of
covariates, if you have a Stata with -margins- and factor variables
try this. (FAQ: always state the Version of Stata you are using).
With older versions, -adjust- will work after -xi: stcox-.
Steve

-- 
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783


*************CODE BEGINS************
webuse drugtr,clear
stdes
set seed 18720
gen v2 = uniform()<.5
quietly stcox i.v2 i.drug c.age, basesurv(prsurv)

** By hand
predict new, xb
sum new if age ==50 & drug==1 & v2==0
sum new if age==60 &  drug==1 & v2==1

** Using -margins: check against the -sum new- results
margins v2#drug , at(age =(50,60)) predict(xb)

matrix ests =r(b)
matrix list ests
local cc =colsof(ests)
forvalues i = 1/`cc'{
gen prsurv_`i' =prsurv^exp(el(ests,1,`i'))
}
sum pr*
******************CODE ENDS***************

On Wed, Jun 2, 2010 at 11:09 PM, Sanam P <sanamp25@yahoo.com> wrote:
> Thank you Maarten for your response
>
> I have been able to estimate the adjusted survivals at individual level using below codes using Professor Yi Li notes at Department of Biostatistics, Harvard SPH:
>
> http://biowww.dfci.harvard.edu/~yili/lect5notes.pdf
>
>
> **baseline survivals:
>  xi:stcox i.var1  i.var2 i.var3  var4, basesurv(prsurv)
>
> predict betaz, xb
>
> gen newterm=exp(betaz)
>
> gen predsurv=prsurv^newterm
>
> However I dont know what should do next to get the adjusted survivals for combinations of the covariates and not at individual level.
>
> Regards,
> Sadaf
>
>
>
>
> ----- Original Message ----
> From: Maarten buis <maartenbuis@yahoo.co.uk>
> To: stata list <statalist@hsphsun2.harvard.edu>
> Sent: Wed, June 2, 2010 5:47:03 PM
> Subject: Fw: st: How to estimate adjusted survival curves after fitting Cox model
>
> --- On Wed, 2/6/10, Sanam P wrote:
>> I was wondering what is the best way for
>> calculating adjusted survival curves after fitting a cox
>> regression model in stata.
>>
>> I think in the Kaplan miere method using "sts graph" and
>> "adjust for " the calculated adjusted curves are for when
>> all the coefficients are equal to zero which is not be the
>> best method.
>
> I am guessing that you are looking for something similar to
> an average marginal effect: i.e. a survival curve that averages
> over the distribution of the explanatory variables. However,
> what is the the distribution of the explanatory variable in a
> survival analysis? The distribution at t=0, or at each individual
> time point. In some sense the latter seems more attractive, but
> notice that changes in the survival curve then also represent
> changes in the distribution of the explanatory variables in the
> at risk population. However, the whole point why we add controll
> variables is that we want to keep them constant...
>
> The trouble is that you are dealing with non-linear models, so
> looking for a single "best way" is usually not fruitful. You are
> much better of by considering the different summary statistics
> possible and find out what it is they say and understand and report
> why they give different results.
>
> 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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