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st: RE: R: partially retrospective survival analysis


From   "Hugh Robinson" <hugh.robinson@umontana.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: R: partially retrospective survival analysis
Date   Thu, 10 Apr 2008 11:49:45 -0600

Sorry to jump in on your question Alan but I have a similar question to yours.  
Do you know how to code a data set so Stata will see a "gap" and not include the subject at risk in that period?
Cleves et al pg. 36 describe the issue but do not cover how to code gaps properly using stset (chapter 6).

HR

Hugh S Robinson Ph.D.
Postdoctoral Researcher
Wildlife Biology Program
College of Forestry and Conservation
University of Montana
Missoula, MT 59802
(406) 243-4128



-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Carlo Lazzaro
Sent: Thursday, April 10, 2008 9:33 AM
To: statalist@hsphsun2.harvard.edu
Cc: 'Feiveson, Alan H. (JSC-SK311)'
Subject: st: R: partially retrospective survival analysis


Dear Alan,

a possible answer to your interesting question is reported in Cleves MA,
Gould WW, Gutierrez RG. An Introduction to Survival Analysis using Stata.
Revised Edition. College Station: Stata Press, 2004: 33-34.

Kind Regards,

Carlo

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Feiveson, Alan H.
(JSC-SK311)
Inviato: mercoledý 9 aprile 2008 21.23
A: statalist@hsphsun2.harvard.edu
Oggetto: st: partially retrospective survival analysis

Hi - Has anyone seen models for or tried doing a survival analysis when
for some observations the data had been observed after the event already
occurred? For example, trying to predict time to heart attack given some
ECG measurements, but for some patients, the measurements were obtained
after the patient already had been in the hospital after the attack. So
in one sense the time to event is actually negative and censored at
zero. Probably a better approach would be some sort of
discrete/continuous mixture model where with a certain probability, the
event has already occurred and given that it hasn't, a standard survival
model takes effect.

Al Feiveson

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