I am currently trying to apply survival analysis to several tree species which were monitored for growth and phenology for 4 years and separated into three treatment groups. From this data I have created a survival variable which gives me the information of whether the individual died during these 4 years or not.
I have thus successfully created for each species data suitable for survival analysis using
survfit functions from the "survival" package to create a R survival object and plot this object for the three treatments.
My question is about how to deal with non events in one treatment group. I have for some species a very small number of events (i.e. deaths) leaving me with quite a high number of non-events (for example, for 360 individuals, only 55 events were recorded across treatments, with one treatment with no events at all).
I have already looked up on the internet how to work with these, and I mainly found that it is ok, the likelihood ratio test is still valid (while the Wald test is not). However, this problem gives me very high values for the hazard ratio (
exp(coef)) in the summary of the
coxph function (like 1.012e+09 associated with a 0.996 p-value, when it is obvious that there is a significant difference between treatments when you look at the plot.)
I was wondering if anyone could help me resolve this problem :
- is it ok to have such high estimates of the hazard ratio (
- does it really reflect the observed difference between treatment, or is the p-value really overestimated ?
Any help with how you would deal with this, or how you dealt with it in your previous experience would be gladly accepted.