# Survival analysis when exact time to event unknown?

My dataset (example here) represents a long-term capture-mark-recapture study, approximately 20 years duration. I am interested in looking at how the survival of animals is influenced by their sex and exposure to viral pathogens. I have data on the age of animals at each capture, but do not technically have data on their time of death, rather if an animal is not captured at a one particular time/consecutive time points they have either evaded capture or have died.

The mean age of animals is <1 year, but some individuals live for up to 7 years. Hence over the 20 year study period several thousand animals enter and exit the population (and enter/exit the study) at very different times.

Question: Can such data be used in survival analysis using a Cox proportional-hazards model, Kaplan-Meier survival curves or similar? If not, does anyone have any recommendations as to how one might approach the analysis of such data? (Considering the question(s) of interest - italic text above). Note that I do not have information on the specific time of event/death.

To date I have modelled this data using mixed models with a gamma distribution, age as the outcome and sex and pathogen exposure as predictors. However, I'm not confident that this is the correct approach. Whilst this compares the time that animals were alive (age) it does not consider the rate at which they may have died - I understand that survival analysis compares both the median time of survival and the rate at which death might have occurred.

• Are you interested in time-varying covariates, like the influence of their weight-changes over time affecting survival? Nov 20, 2020 at 3:58
• This would be interesting, but only be a side interest and not a main interest. Thanks @Cam.Davidson.Pilon Nov 20, 2020 at 5:30

• in R, there is flexsurvreg
• in Stata, there is merlin
• in Python, there is lifelines (I'm the author)
• Thanks @Cam.Davidson.Pilon. If I understand correctly, my model essentially just becomes a GLMM or GAMM then? Where the outcome is Age, fixed effects are Sex, Pathogen exposure and Weight, and a random intercept for Individual animal. In which case, such a model could also be implemented in lme4 or glmmTMB? Nov 22, 2020 at 21:54
• It's a GLM with censoring - so you might need to specifically use a survival analysis library if lme4, etc., don't support censoring. Nov 23, 2020 at 2:51