[R] modelling a particular design

Andreas Bartsch bartsch at neuroradiologie.uni-wuerzburg.de
Sat May 11 18:06:21 CEST 2002

Dear R- and Omega-list-members,
I am trying to make statistical inference about the following design:
A dependent variable y has been measured multiple times, i.e. 4 times
(y1,y2, y3, y4), unfortunately suffering from some successive dropouts (i.e.
the sample sizes varies for y1, y2, y3, and y4). For every y, two other
variables (covariates) were also measured: x & z, and both do presumably
exert an effect on y. At some cutoffs, x can also be trichotomized into 3
ordinal levels constituting a presumed factor (fx) influencing the level of
y at all of the different measurings (1-4). x and fx are rather stable
arcoss the 4 measurements whereas z is not. H0 is that x and fx are not
influencing the level of y (i.e. explaining any variance of y) irrespective
of  (i.e. controlled for) z at any of the measurements.
(1) What would be a good way of testing for the hypothesis in the context of
a GLM or a canonical regression analysis? I can see that in a parametric
testing and for a single measurement of y a simple multiple linear
regression (MLR) or an ANCOVA (or a RANCOVA in a nonparametric approach)
would do the trick. However, I am not sure how to tackle the issue facing
repeated and at least biologically somehow interdependent measurements and
the goal to include as much measurements as possible even though the sample
size differs. I thought about running an MLR for x and an ANCOVA for fx
seperately for y1, y2, y3 and y4 but I am not sure if this would require a
correction for multiple testings and if this is in fact the best approach at
all. Pooling all 4 measurements, on the other hand, would mean to pretend
that they were all derived from different subjects which is clearly not the
case. So, basically I don´t really know how to treat the testing.
(2) Is there an implementation in R or Omega to perform such testing? How
would that run?
Hopefully, the question isn´t too trivial to the list - I am not a
statisticician and just fed up with the comercial stats software... Thank
you very much in advance-
Andreas Bartsch, MD

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