Sir,
This kind of analysis is new to me, s oI m sorry if I m asking "stupid"
questions below...
In my study, patients might have been sick for years before the
diagnosis is made, then they can die few months later, once diagnosed,
either from their disease or because they are elderly patients;
however, they can also survive for decades.
I d like to know the impact of a "newcovariate" on their survival, and
in a second step, adjust for know prognostic factors to check whether
this new covariate is an independent prognostic factor as well.
So according to my readings it seems to me that using left truncated
right censored models is the best way to test my hypothesis ... I hope
I m right there !
after reading the help file of the survival package am I right to say
that I have to use the coxph function to do this kind of analysis ?
using the following formulation: coxph(Surv(time, time2, event, type =
interval, origin = 0))~ newcovariate,....
in this case if a patient is 54 years at the time of diagnosis, and
dies at 55, is it right to code this one
time= 54, time2 = 55, event=1
then if I have patient diagnosed at 66 and followed up to 68 without
any relevant event (censored) I m also right to code this one:
time= 66, time2= 68, event=3
I dont fully understand the option of using "interval2" for "type"
option, could you comment a little more=2
0on this please...?
last question: what is the best way to check that the left truncation
data and the failure times are independent ? just a scatter plot ?
regression ?
thanks for your help
yours sincerely
Philippe Guardiola
-----E-mail d'origine-----
De : Thomas Lumley <tlum...@u.washington.edu>
A : phguard...@aol.com
Cc : r-h...@stat.math.ethz.ch
Envoyé le : Jeudi, 26 Février 2009 8:24
Sujet : Re: [R] left truncated data survival analysis package
On Thu, 26 Feb 2009 phguard...@aol.com wrote:
Hello,
I d like to run a survival analysis with "left truncated data". Could
you recommend me a package to do this please ?
The 'survival' package.
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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