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RE: st: rmanova or anova with repeated command, what to use?

From   "Pieter-Jan" <>
To   <>
Subject   RE: st: rmanova or anova with repeated command, what to use?
Date   Wed, 5 Oct 2011 20:36:58 +0200

Dear David,

Thanks for your extensive reply. Indeed, we did some graphics and in some of
those some interesting trends were seen. Your suggestion of using xtmixed
instead of anova is interesting and worth trying it. Again, many thanks for
your reply and suggestion.

Sincerely Yours,

Pieter-Jan van Ooij

-----Oorspronkelijk bericht-----
[] Namens Airey, David C
Verzonden: 5 oktober 2011 5:02
Onderwerp: re: st: rmanova or anova with repeated command, what to use?


Sounds like a great experiment! One thing I would do is to start with graphs
if you have not. For example, make a 3 panel graph (baseline, placebo,
active) where each panel plots each patient as a connected line of 7 points
(t0-t6), so you have 13 lines per panel. You can also make 13 separate
plots, one per patient, with 3 connected time lines for each treatment, to
get a detailed sense of the effects per patient over the 3 conditions.
Plotting all 13 x 3 = 39 lines in one plot may be too busy, but you could
color by treatment. You can simplify this last plot by collapsing across
patients to means for each time point and plot 3 average time profiles
(baseline, placebo, active). I think this will help you "see" what's going
on before you try to model the data, and you'll have a sense of whether the
model matches what might be obvious in the plots. The plots will also show
outliers, problems with variances, etc. that you might want to consider.

        Baseline Placebo Active
        t0-t6    t0-t6   t0-t6

If I'm understanding the design, you have two repeated measures variables,
treatment condition (baseline, placebo, active; 3 levels) and time (pre, 0,
2, 4, 8, 12, 22 hours after exposure), where every patient sees every
treatment by time combination giving you a total of 13 x 7 x 3 observations.
Presumably you balanced the order of treatments. You could also have an
order factor in the model if you wanted to check that. Leaving it in may
soak up some of the error variance. Anyway, if you wanted to model both
group and time, you probably would want to treat time as at least an ordered
factor, if you did not want to give each person's treatment by time profile
just a slope and intercept. The t0 time point "pre" may be used directly or
it may be used to form differences with t1-t6, and or could be used as a
baseline covariate. The Stata xtmixed command is something you should
consider using instead of anova or manova, but it's complicated enough that
a statistician's help is n!
 eeded, unless you have an example where your design was analyzed and you
can emulate.

It sounded like you plan to do repeated measures ANOVAs at each time point.
I think this is going to miss seeing what is going on.

> Dear statalisters,
> Some time ago we performed a randomized crossover study in which we
> monitored the lung function of a group of volunteers  during three days.
> 1 was used to determine baseline lung function whereas day 2 and 3 were
> to monitor lung function after inhaling either placebo (air) or active gas
> (oxygen) under hyperbaric  condition. During each measurement day lung
> function was measured 6 times. All variables and observations were put in
> dataset which initially had the following format:
> ID           Group      Time                     Result1 etc
> 1             0          0                             6.19
> 2             0          0                             5.97
> .             .          .                              .
> .             .          .                              .
> 13            2          22                           5.33
> Etc
> ID exist of 13 persons (nr 1-13)
> Group: 0 (baseline), 1 (placebo), 2 (active)
> Time: pre, 0, 2, 4, 8, 12, 22 hours after exposure
> We want to perform a repeated measures anova as all subject performed all
> three test days which makes the groups not independent. The format we had
> mind was to search for differences between the groups at a specific time
> point and to look for a correlation between Result1, etc and variable

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