Eduardo Nunez<[email protected]>:
Death is not censoring here; it is the true end of risk for increases
to the duration "days alive and out of the hospital" but the end of
the observation period is a censoring event. You want a survival
model here, I think, and you could run it as single risk by
subtracting days in the hospital from the endpoint, but a asymmetric
competing-risks multiple-failure model is the underlying model (death
prevents you from admission to the hospital, but admission to the
hospital do not prevent death). Do you observe time-varying
characteristics of patients over time? See also:
-help st-
http://www.iser.essex.ac.uk/iser/teaching/module-ec968
http://home.fsw.vu.nl/m.buis/wp/survival.html
etc.
On Thu, Aug 6, 2009 at 11:18 AM, Eduardo Nunez<[email protected]> wrote:
> Stata/SE 11.0 for Windows (64-bit x86-64)
> Born 13 Jul 2009
>
> Hi all,
>
> I would appreciate if someone can advise me on the best way to analyse
> and endpoint that is based on counts with censoring mechanism. It has
> been suggested in heart failure trials to use as endpoint "days alive
> and out of the hospital" in order to evaluate new treatment
> modalities. This composite endpoint is supposed to capture the burden
> of mortality and hospital stay (for rehospitalization) during the
> follow-up period. Patients will be censored either by death or lost to
> follow--up. I was thinking in censored-poisson, although I doubt the
> day-counts follows a poisson distribution. Moreover, using simple
> regression analysis won't take into account the censoring mechanisms.
>
> Regards,
>
> Eduardo Nunez, MD, MPH
> Servicio de Cardiología, Hospital Clínico Universitario, Universitat
> de Valencia., Spain
>
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