Hello everyone,
I tracked transmittered birds weekly throughout the year with the goal of using known fate models to estimate survival. My data therefore consists of an encounter history of 52 occasions and several individual covariates such as sex, age, and study site to build comparative models. I would also like to build a model that uses "season" as a covariate to see if survival is different between seasons (breeding, migration, overwintering). I have considered the following approaches:
1) Create separate rows for each individual in each season (ex. Bird1 Breeding, Bird1 Fall Migration, Bird1 Winter all as different rows), but this causes nonindependence of samples and differing numbers of occasions in the encounter history.
2) Add time-dependent covariates to specify which occasions are part of which season: Columns "Season1" through "Season52" would have either 0 (Winter), 1 (Spring Migration), 2 (Breeding), or 3 (Fall Migration). Unfortunately, I believe this would treat the values as continuous (as numbers 0-3) instead of categorical. From reading about time-dependent covariates, I have not found an instance where a categorical covariate was used in a time-dependent manner.
3) Add time-dependent covariates with dummy variables to compare only 2 seasons to each other: Columns "Winter1" through "Winter52" with either a 0 or 1 value; columns "Spring1" through "Spring52" with either a 0 or 1 value; etc.. We could then test if spring (or any season's) survival is different from all other seasons, but wouldn't be able to use "season" in our model comparison.
Could anyone guide me on a way to move forward with separating our full-year data into seasons in a way that would allow me to use season in our model selection approach? Does this require multi-state models?
Thank you very much in advance,
-Mercy