I am working with 10 years of radio-monitoring data where fates of animals (i.e., live/dead) were determined monthly and entries of animals into the study are staggered. My objective is to estimate survival. Originally, my approach was to use the known-fate data type in MARK. After compiling the histories, I see numerous issues with the data that may preclude use of Pollock's staggered entry design. In particular:
1) Monitoring data was not collected during approximately 20% of the months during the study and sometimes not for several consecutive months
2) Individual animals often were monitored, lost their transmitters or were not monitored for numerous consecutive months, and then re-entered the dataset when captured and recollared with a new transmitter or monitoring for them resumed
3) Some individuals occasionally went missing for a month or 2 because monitoring personnel could not find them
Because the missing data is so extensive, the amount of censoring likely would be problematic. I considered using Burnham's joint live-dead model, because the recapture probabilities for months with missing data could be fixed to 0. However, from my understanding, that model would not appropriately handle the trailing pairs of zeros for many animals whose transmitters were known to have dropped off while they were alive and were never seen again (i.e., right-censoring In known-fate). I also considered using mult-state live-dead models in some fashion, but cannot determine off hand if that is possible.
Does anyone have any helpful suggestions of how to deal with these missing data/censoring issues or can anyone point me in the right direction to other similar applications?
Many thanks in advance!