I am using winbugs to fit a joint model of a set of weight measurements over
time with two adult outcomes BMI and plasma glucose for each individual.
Interest lies in quantifying the correlation of the adult outcomes
with the growth parameters.
The growth model is parameterized as a simple 2-level (individual-occassion)
polynomial function of the occasion timing and the intercepts and slopes are
I am not quite sure what's the best way to proceed with the joint modelling
of the two adult outcomes (2-level or individual outcomes) with the growth
parameters (2-level individual level parameters)
I have tried 2 different ways:
1. specify a multivariate normal (MVN) for the outcomes and the parameters
with a wishart-prior for the variance-covariance matrix as specified in mlwin
Winbugs proceeds with the estimation, although that DIC is greyed out. So, I
am a bit worried about what's going on.
2. Specify univariate normals for the adult outcomes and then specify an MVN
for the means of these two outcomes and the growth parameters. Now, winbugs
is happier and there DIC is calculated - I do have though the added
complication to specify a prior for the precision of the means of the two
adult outcomes. In other words, now I am specifying a measurement error
model for the adult outcomes. So, for the prior for these precision
parameteres I could either give a fixed value (something large) or a
gamma(0.001,0.001). The second of-course won't work very well, slow
convergence because there is no information in the data for this. For the
first specification however, I was expecting not to be very different from
1. But there are quite large differences.
There are 1000 individuals with 8459 observations.
I was wondering if anybody has any thoughts on how's best to proceed.
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