On Sep 16, 2010, at 4:43 PM, David Winsemius wrote:
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.
As a further note: There is a nice paper by Atkinson and colleagues at
the Mayo Clinic with R/S code for analyses
"Poisson models for person-years and expected rates", Elizabeth J.
Atkinson, Cynthia S. Crowson, Rachel A. Pedersen, Terry M. Therneau.
Technical Report #81
http://mayoresearch.mayo.edu/mayo/research/biostat/upload/81.pdf
You will be shown how to set up the time intervals (and population at
risk if that happens to change materially) as offsets.
--
David Winsemius, MD
West Hartford, CT
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