Our research group has done a wide variety of performance measurements on lab mice (running speed on treadmills, oxygen consumption, spontaneous activity, etc. etc. for each mouse). We also have survival and censoring information for each mouse. I believe that our measurements reflect several underlying biological processes that in turn determine the rate of aging in these animals.
I'm new to SEM, but from my reading so far it sounds like the measurements are manifest variables, the putative aging processes they report on are latent variables, and this would be exactly the sort of problem for which SEM is intended. I assume that as I read more about SEM, I will learn how to formulate candidate models for these latent variables and how determine which are best supported by the available evidence.
But then I want to use the latent variable estimates as predictor variables in a survival model, and see which ones are most predictive of hazard. In 2009 another user started a thread called "Survival model". My requirements are more flexible than his. I can use a Cox model, and experience shows that the Weibull model is not a horrible fit for mouse longevity data. Not as good a fit as the Gompertz model, but easier to work with and interpret.
At any rate, I'm not committed to a particular survival model; I am interested in searching for the best possible predictors of hazard and/or longevity given the data we have collected. Would you folks recommend extracting the estimates I obtain from OpenMx and simply using them as predictor variables in, e.g., coxph() or survreg() or does OpenMx have the ability to do survival regression within the same model that estimates these latent variables?