I have a dataset with destructive follow-up. That is, with a population starting at time 0, we are taking out a proportion at predetermined time points to see whether the event has occurred to them. The sampling is destructive, so we can't sample the same individuals repeatedly. In this case, we need to dissect a fish to determine whether the event has happened and we do this to a set number of fish at predetermined times. For example, we dissect 15 fish at 24h to know which animals have had the event, then we dissect another 15 animals at 48h and so on. I have done a logistic regression with time as one of the predictors and binary outcome (binomial family glm), but I wanted to ask if it's possible to use survival analysis for this type of data. I think what I have is left censored data for the animals where the event has occurred at time of dissection, and right censored for the animals where it hasn't occurred. In that case, there is only censored data, right? Is there a correct way of using this kind of data for survival analysis?
Edit: In this experiment, I have size of fish and temperature as covariates, so ideally, I would like to test whether these two variables affect the time to event. Both temperature and weight can be stratified ("small, medium, large" and "cold, medium, warm") as they are semi-controlled variables, but I'll probably get more information out of using the exact measurements rather than creating dummy variables. I can also add that the event is certain to happen eventually with the experiment designed to keep going until >95% of the fish are expected to have had the event. I also know for certain that none of the fish had the event at time 0. This then also would suggest that I have reasonable priors, so could take a bayesian approach.