R help - Sep 2010 - Survival Analysis Daily Time-Varying Covariate but Event Time Unknown

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

Thank you for help or redirection.

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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