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Raymond Hoffmann
Professor
Medical College of Wisconsin
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While I haven't looked at RSA as an outcome, heart rhythms should be stable (variance near 0) with high values indicationg considerable risk. So this is a good measure of an arhythmia severity. Thus a gamma GLM with a lo would be my first choice (make sure you look at the goodness of fit, it could be log normal) and the corresponding GEE, generalized estimating equation, or a GLMM, generalized linear mixed model for the repeated observations. Usually a log link is chosen as the default, but you might use an identity ling since it is already log transformed or untransform the data. Look carefully at the actual transform; there probably won't be any zero's, but of course the log of zero or near zero is not well behaved (- infinity!). The GEE is a marginal model while the GLMM is a individual level model, so it depends on which is more appropriate to what you are modeling.
Again it also depends on the number of subjects and the number of follow-ups (numerical stability of the algorithms), so it would be very helpful if you described the problem in more detail. Clearly there are a lot of choices along the way, e.g. the variance-covariance model, whether the time points are regular or irregular, etc.that have substantial effects on the model choice.
Among the better References for this are:
SAS's book on Mixed models by Littell et al,
Stata's book by Hardin and Hilbe or the one (I haven't had a chance to review the new two volume version) by Rabe-Hesketh.
Fitmaurice and Laird's book on Longitudinal Models and
Applied Mixed Models by Brown and Prescott
All of these cover the range of problems with good examples and explanations and enough, but not too much, theory.
Ray