Question: Could anyone give me hints as to a model framework that can be used in the following setting:

The outcome A is dichotomous. I want to investigate the effect of a continuous variable B and a continous variable C on A in a longitudinal setting. So far so good. 

The problem is that C depends on B, in that values of B above a threshold (Unfortunately we do not know the threshold and the threshold itself might not be constant over time) will change C. The change in C will furthermore reduce B in the next measurement.

How can I decipher the isolated effect og B and C on A? 

Would a mixed-logistic-regression model be OK?

ie;t=time

A~B\*t+C\*t+B\*C\*t

Any other ideas?

//M

Answer: 
Not easy at all. This starts to sound like the sort of thing that [Jamie Robins](http://en.wikipedia.org/wiki/James_Robins) and colleagues have done a lot of work on. To quote the start of the abstract of one of their papers:

"*In observational studies with exposures or treatments that vary over time, standard approaches for adjustment of confounding are biased when there exist time-dependent confounders that are also affected by previous treatment.*"

(Robins JM, Hernán MA, Brumback B. Marginal Structural Models and Causal Inference in Epidemiology. *Epidemiology* 2000;11:550-560. <http://www.jstor.org/stable/3703997>)

In their example, low CD4 count means you're more likely to get anti-HIV drugs, which (hopefully) increase your CD4 count. Sounds very much like your setting. 

There's been a lot of work in this area recently (i.e. more recently than that paper), and it's not easy to get to grips with from the journal papers. [Hernán & Robins are writing a book](http://www.hsph.harvard.edu/faculty/miguel-hernan/causal-inference-book/), which should help a lot -- it's not finished yet, but there's a draft of the first 10 chapters available.
Your problem is one of multi-collinear regressors (since B and C are correlated). I would suggest that you look at the answers to the question: [Dealing with correlated regressors](https://stats.stackexchange.com/q/3561/28). 

The following paper may also be relevant in your context: [Using principal components for estimating logistic regression with high-dimensional multicollinear data](http://dx.doi.org/10.1016/j.csda.2005.03.011)