Can structural equation models be used to derive clinical formulae? Structural equation models (sem) are used to model latent variables. Renal function is a latent variable measured by serum creatinine levels (with measurement errors) expressed by many different clinical formulae derived from linear regression models. Inulin clearance is a gold standard for renal function. However, hundreds of articles had studied the relative "accuracies" of these formulae as compared to surrogate "gold standards". Can sem be used to derive a clinical formula from serum creatinine and inulin clearance for the estimation of renal function?
 A: If you can build a regression model for something, it means to me that this is a measurable quantity. While a linear regression model is a special case of the general SEM, the greater strength of SEM is, arguably, being able to accommodate latent variables and measurement error in predictors. If you want to build a structural equation model for the latent variable BLAH (renal function), you need to have a study where several different ways to measure it have been undertaken (serum creatinine levels, inulin clearance). If, in turn, these variables are obtained from "clinical formulae" (another regression model, as far as I understand), that messes up the model quite a bit, and you need to figure out exactly which of the variables affect the measurement process, and which one affects the underlying latent variable (without knowing anything about the biochemisty of whatever it is that you are interested in, I would dare suggesting that age and gender affect the latent variable, rather than the measurement process).  Ideally, you would want your equations linking the latent variables with the measured variables to be linear in parameters, so you would need to apply the typical transformations, such as logs. The standard language of structural equation models are path diagrams; mainstream statisticians tend to stare at them with little understanding of what's going on, but social scientists have found them to be very handy in explaining the relations between variables. I think this paper explains it quite well, although I don't know how closely it follows the biomedical language (it is written by psychometricians): http://www.citeulike.org/user/ctacmo/article/2663951. 
A: Yes SEM can do that. You enter the measured and latent variables into the model, specify their relationships, and then you will get quite a lot of output. This output will include a structural equation (looks like a regression equation, with coefficients, standard errors, etc) and an R^2 result. SEM also allows you to specify other relationships such as allowing covariance between serum creatinine and inulin.
