I have a large dataset of within-individual repeated measures, where each participant has varying number of observations (multiple rows of data for each participant). Data include a time-dependent marker variable (dynamic risk), time-to-event data (recidivism), as well as demographic information and a separate risk score based on historical factors (static risk)
I am using JointLCMM to model trajectories of a marker variable (dynamic risk) while accounting for data missing due to survival events (recidivism). I am a bit confused about whether the variable used in the survival argument must be time independent.
As I had not been including covariates in the mixed-effects model
m<-JointLCMM(dynamic_risk~time,random = ~ time, subject = 'ID', mixture = ~ time,ng = 2, idiag = TRUE, data = data, link = "linear")
I hadn't thought to include them in the survival argument
Reading more on JointLCMM, it seems that this may be problematic, as my dynamic_risk variable is time-dependent.
Is it essential that the variables in the JointLCMM survival term be time-independent?
If time-independent covariates are essential for the survival term, should they be included in the mixed model as well?