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st: R: Kaplan Meier graph in longitudinal data


From   "Carlo Lazzaro" <carlo.lazzaro@tiscalinet.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: R: Kaplan Meier graph in longitudinal data
Date   Tue, 12 May 2009 09:36:58 +0200

Dear Deepa,
provided that the proportional risk assumption holds, a possible solution
would be to switch to the semiparamentric Cox regression model, with the
option -cluster(patient)- (please, see -help stcox-). In this way, you would
correctly assume that patients are independent, whereas receuurences within
the same patient aren't.
This topic (like many others) is covered in Cleves MA, Gould WG, Gutierrez
R. An Introduction To Survival Analysis Using Stata. Revised edition.
College Station: StataPress, 2006: 148-152.

Another possible (but trickier option) would be considering a Markov model
(please, see Sonnenberg FA, Beck JR. Markov models in medical decision
making: a practical guide. Medical Decision Making 1993;13:322-339.

HTH and Kind Regards,
Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Deepa Aggarwal
Inviato: lunedì 11 maggio 2009 19.52
A: statalist@hsphsun2.harvard.edu
Oggetto: st: Kaplan Meier graph in longitudinal data

 Hi All,

I have the following longitudinal data :

 id     d_entry           d_censor         status          age       x2
 1      20jan2008     22jan 2008        0               62          0
 1      22jan 2008    24jan 2008        0               62          0
 1      24jan 2008    26jan 2008        0               62          1
 1      26jan 2008    28jan 2008        1                62          0
 2      13jan 2008    18jan 2008        0               70           0
 2     18jan 2008    20jan 2008        0               70           1
 2      20jan 2008    24jan 2008        0               70           0
 2     24jan 2008    26jan 2008        1                70          1

 Here id is patient id number, d_entry is the date of entry,  d_censor
 is the date of censoring, status is the censoring variable, age is
 fixed for each id, x2 changes with time for each id.

First I stset the above mentioned data by using the following command:
stset d_censor, id(id) failure (status==1) origin (time d_entry)

Now I want to get a Kaplan meier graph . I know if there is one event per
id, then sts graph can be used.
But in recurrent event models, what command should be used?


 Thanks for your consideration.

 Deepa



-- 
Deepa

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