How to use joint models to assess the unique contribution of change in a longitudinal variable, incremental to current levels of that variable?

Question

I am hoping to use joint modelling (shared random effects models using JM) to test whether change in a longitudinal marker variable contributes to prediction of a survival event, incremental to current levels of that same marker variable.

In other words, if two participants have the same score on a marker variable, do they have the same risk of experiencing the survival event, if the score represents a decrease for one and an increase for the other?

In a Cox regression, I would do this by entering both current score and change score (e.g., difference between marker variable at assessment time and prior two weeks) as time-varying predictors to see if change remains a significant predictor after accounting for current score, but I am not sure how to adapt this to a joint model. Would it make sense to use a change score as my longitudinal marker variable, with current score as a covariate of that(change~time+current)? Would the reverse make more sense (current~time+change)? Do neither make sense and it's a stupid question because prior scores are explicitly factored into joint models, thus embedding change?

Any help is appreciated!

Answer 1

The parameterization option for jointmodel allows users to specify if they'd like to include slopes of a marker variable, current levels, or both to determine risk for the survival event. To test whether slopes incrementally predict the survival event, I would set the argument to parameterization="both"