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?
Best Answer
The major breakthrough of the joint models relative to the time-dependent Cox model is that they allow one to deal with the error measurements in the time dependent variables (longitudinal variable in this case). In a Cox model with time dependent covariates we assume that the variables are measured without error.
Some references:
Tsiatis, A. A. e M. Davidian (2004). Joint modeling of longitudinal and time-to-event data: An overview. Statistica Sinica 14 (3),809-834.
Rizopoulos, D. (2012b). Joint Models for Longitudinal and Time-toEvent Data With Applications in R. Chapman and Hall/CRC
Henderson, R., P. Diggle, e A. Dobson (2000, Dec). Joint modelling of longitudinal measurements and event time data. Biostatistics 1 (4), 465-480.