Hello.

I have a problem that I have not been able to solve within the last months.
Maybe R offers options to tackle this.

My dataset:

* N = 1000, 5 measurement points
* 9 categorical dependent variables (depressive symptoms, scored 0, 1, 2 or
3) that are intercorrelated
* 7 time-varying covariates that are dichotomous life events (yes/no) at
each measurement point (people can have multiple life events, therefor this
is not just one categorical with 0=no life event, 1=life event1, 2=life
event2 etc.)
* A bunch of time-invariant baseline predictors (e.g. neuroticism, early
family environment, genotype, gender, family history of depression ... ),
measured only once at the first measurement point.

My core question is whether different life events lead to different
depressive symptom profiles (controlling for baseline variables).

I can confirm this hypothesis in nine univariate repeated measurement
mixed-models (some life events are predictors for some symptoms but not
others, and vice versa), but that invites the problem of (1) not being able
to control for multiple models, and (2) ignoring the correlated structure
of my categorical response variables.

So I'm looking for multivariate multilevel models currently to do all of
this in one analysis.
Could this be achieved with R? The more palpable the recommendations the
better, I have little experience with R.

Thank you
Eiko

	[[alternative HTML version deleted]]

