I have a research question where I investigate the links between a continuous changing variable X (taken as a log of a variable) and mortality. This variable X is measured every three years and in my research question, I wanted to see if the increasing/decreasing nature of this variable (the fluctuations/changes) predict mortality.
I opted out against using the Cox Proportional Hazards Model with time-dependent covariates because of the complexities it carries and as it doesn't quite fit my research question. I want to produce a single measure of change in this variable in time for every participant (designated as sortnr below) and I tried that using the mixed models approach in R as:
The output of the model gives me random effects for both the intercept and the variable time. Now, is it correct to consider the random effects for time variable is the individual change over time by in my dependent variable X? Or is it proper to think that the change in variable X in time is the fixed effects of time added with the random effects of time. I have also seen that there is a
coef(model.1) function that gives a different set of values than the
I am not sure how to produce this change estimate definitively and whether the approach I am attempting is viable. I am very new to mixed models analysis and still learning some of its theoretical underpinnings so I would really appreciate any help regarding this. Thank you!