Dear list,
I have data on insect survival in different cages; these have the
following structure:
deathtime status id cage S F G L S
1.5 1 1 C1 8 2 1 1 1
1.5 1 2 C1 8 2 1 1 1
11.5 1 3 C1 8 2 1 1 1
11.5 1 4 C1 8 2 1 1 1
There are 81 cages and each 20 individuals whose survival was followed
over time. The columns S,F,G,L and S are experimentally manipulated
factors thought to have an influence on survival.
Using survfit(Surv(deathtime,status)~cage) gives me the survivorship
curves for every cage. But what I´d like to have is a mean survivorship
value for every cage.
Obviously, using tapply (deathtime,cage,mean) gives me mean values, but
I´d like to have a better estimate of this using a proper statistical
model. I´ve tried a glm with poisson errors (as suggested in Crawley´s
book, page 628), but the back-transformed estimates (using status as the
response variable and deathtime as an offset) were totally unrealistic.
As I´m new to survival analysis, it would be great if anyone could give
me some hints on what method would be best.
Thanks a lot!
Christoph
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