I have time-to-event survival data (ie., start, end, fate [death or censor] for each known individual). I am looking to model survival for a population of animals that are released onto a new landscape using Cox Proportional Hazard Models where I include additional demographic predictors (sex, age class) and potentially time-varying environmental predictors (temperature, precipitation).
However, beyond modeling survival, I am most interested in estimating when a break point might occur in estimated survival rates relative to the start of study. That is, we hypothesize there will be a period of acclimation where survival is lower - but that once animals acclimate to their new landscape, survival will increase. What is the best way to estimate the unknown duration of an acclimation period (i.e. what is the best way to estimate when an unknown breakpoint in baseline hazards occurs)?
I've seen this done (with code provided even! - citation at bottom) for a Bayesian formatting of a band-resight Cormack-Jolly-Seber model - but I'm not sure how to adapt it to a time-to-event analysis. These authors treated acclimation period as parameter in their model and their full model included a logit link function between two survival models - one with and one without an acclimation effect depending on if during or after the simultaneously estimated acclimation period.
Any suggestions would be SO appreciated. I'm in semi-early stages but feeling overwhelmed.
(Armstrong, D. P., Le Coeur, C., Thorne, J. M., Panfylova, J., Lovegrove, T. G., Frost, P. G., & Ewen, J. G. (2017). Using Bayesian mark-recapture modelling to quantify the strength and duration of post-release effects in reintroduced populations. Biological Conservation, 215, 39-45.)