I've got a methodological question, and no data set attached.
Suppose I aim to fit a proportional hazards model (Cox) for survival data. I have multiple observations for each individual (data in long format). Particular interest lies within one continuous predictor (such as blood lipid levels) and I'm examining the association/effect on risk of myocardial infarction.
I'm a newbie to this, but I use time-dependent Cox regression (without clustering since only one event is analyzed [repeated events are not of interest]). It appears to me that this is the standard method.
Now, the packages JMbayes, JM, joinR, lcmm can fit joint models (http://www.r-bloggers.com/joint-models-for-longitudinal-and-survival-data/) which appear to be a fusion between mixed effect models and Cox regression.
This seems like a nice idéa, to combine to robust methods... A couple of advantages are reported for Joint Models, of which the "precision in each patients trajectory" is repeatedly mentioned. Howeer, I searched pubmed, google scholar and google for publications using this approach and did not find much.
Should I stick to the "usual" time-dependent (counting process) Cox regression? Advantages? Drawbacks?