On Sep 16, 2010, at 12:14 PM, smm7aa wrote:
Help!
I am unsure if I can analyze data from the following experiment.
Fish were placed in a tank at (t=0)
Measurements of Carbon Dioxide were taken each day for 120 days
(t=0,...120)
A few fish were then randomly pulled out of the tank at different
days,
killed and examined for the presence of a disease
T= time of examination in days from start (i.e. 85th day), E = 0/1 for
nonevent/event
My problem has been linking all the Carbon Dioxide measurements up
to the
day of examination and trying to create a survival object.
I have considered interval censoring with right censored for fish
without
disease and then left censored for fish with the disease, but i really
cannot structure the data or intuitively figure out how to
incorporate the
daily Carbon Dioxide values up until day of examination.
The end goal to to predict an event based on Carbon Dioxide levels
I think the goal should be restated as estimation of the proportion of
disease in the population as a function of time and CO2 concentration.
I think Poisson regression would be sensible analysis framework. I
don't think you need to consider censoring unless your repeated
sampling has removed a substantial proportion of the starting
population.
?glm # with family="poisson"
Poisson regression is a proportional hazards framework that is
suitable for grouped data such as you have. You do need to ask whether
recovery is possible from a diseased state and what sort of analysis
you will apply to individuals who died during hte study period, but
those are domain questions, as much as statistical questions.
David Winsemius, MD
West Hartford, CT
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