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| Author: |
Richard Ulrich |
| Subject: |
Re: Nonparametric longitudinal analysis |
| Body: |
On Mon, 12 May 2008 08:13:04 -0700 (PDT), W <wzhang999@gmail.com>
wrote:
> On May 9, 6:35 pm, Richard Ulrich <Rich.Ulr...@comcast.net> wrote:
> > On Fri, 9 May 2008 13:42:35 -0700 (PDT), W <wzhang...@gmail.com>
> > wrote:
> >
> > > Hi All:
> >
> > > I have a longitudinal data with 2 factors: starin and age, and a
> > > covariate bodyweight. Each subject was measured at 0min, 30min, 60min,
> > > 90min and 120 min.
> >
> > That sounds simple enough, but I have no idea what
> > "starin" is (and Google doesn't help). Thus, I have no
> > natural notion of what is being measured, and whether it
> > grows/ sinks rapidly, or what.
> >
> > You can usually get better advice when we readers have
> > a better idea of what you are trying to accomplish, and
> > with what data.
>
> I apologize. "starin" should be "Strain".
>
> This was an experiment of glucose tolorence test on mice. Glucose was
> measured at 0, 30, 60, 90 and 120minutes. The mean at each tome point
> was about 177, 537, 516, 512, 508. Time 0 is when glucose was applied.
>
> >
> > > The data were quite right-skewed due to
> > > truncation(the lab machine can only read up to 600unit).
> >
> > "Right-skewed" in statistics means that there is the
> > long tail to the right, with the bulk of data on the left.
> > I think you describe "left-skewed". (This does contradict
> > a certain natural-language use of skewness, such as,
> > "attitudes skewed towards Democratic" which would place
> > the bulk as Democratic.)
> >
>
> Apologize again. I mean it's left-skewed. The response was truncated
> at 600 (81 out of 240), which was the ceiling the machine can read.
> The Skewness was -0.71 and the Kurtosis was -1.1. Do you think I can
> still do the analyses as you proposed above? One thing I was not sure
> was how I can handle the correlated structure of the repeated measures
> at the 5 time points, which can be easily taken care of by sas proc
> mixed if I have a normality assumption for the reponse.
>
Okay. One thing that seems *apparent* from the means
presented is that the 0-time is low, and the highest point is
the first measurement after that. If that was not a stupid and
fatal truncation of the data, then the "effect" you are investigating
would be essentially uninterpretable from the general repeated
measures analysis --
1) There is a difference between times.
2) There are linear, quadratic, cubic, and quartic terms that
are significant. Or some combination of most of them.
So what?
What seems to be interesting, if anything, is that there may
be a decline after the initial boost. For that, you use an
analysis of the latter four times. Look at the linear trend.
That test is pretty robust.
I suspect that the heterogeneity of variance will not be
bad for these 4 times, and there is the conventional correction
available for the overall repeated-measures F-test d.f. (if you
want to look at that test), since 80 or so measures (out of 192)
are truncated at 600.
--
Rich Ulrich
http://www.pitt.edu/~wpilib/index.html
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| Topic: |
Nonparametric longitudinal analysis |
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| Message: |
Author |
Date |
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*Message 1* |
Richard Ulrich |
Mon, 12 May 2008, 9:28 pm |
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