From Ricardo Ovaldia To statalist@hsphsun2.harvard.edu Subject Re: st: Experimental design - ANOVA/GLM? - please help Date Thu, 26 Sep 2002 15:23:27 -0700 (PDT)

```Exactly what I needed. Thank you, Ken!!!

Best,
Ricardo.

--- khigbee@stata.com wrote:
>
> > I was approached by an investigator with the
> following
> > problem. He had two groups of experimental rats,
> 10
> > diabetic and 10 non-diabetic. Each of these rats
> > one liter of 10 pups (average). On each of the
> pups a
> > series of biochemicals were measured. He wants me
> to
> > compare the mean value of these biochemicals from
> the
> > pumps from diabetic moms to the pups from
> non-diabetic
> > pups. He then suggested that I do a simple t-test
> > comparing the means of the two pup groups. I
> pointed
> > out that the observations are not independent
> because
> > of several pups from the same liter and that the
> liter
> > effect needs to be taken into account.
> >
> > How can I set this up in Stata?
>
> Assuming I understand your setup, you have a nested
> ANOVA model.
> I ran the following commands to create some fake
> data that I think
> has a similar form as yours.
>
>         clear
>         set obs 2
>         gen group = _n
>         expand 10
>         sort group
>         qui gen mom = _n in 1/10
>         qui replace mom = mom[_n-10] in 11/20
>         set seed 32981
>         gen z = 10 + round(uniform()*4-2,1)
>         expand z
>         drop z
>         bysort group mom : gen pup = _n
>         gen y = uniform()*8 + group
>         compress
>
> I then looked at a table of pup counts and y value
> means for each
> group (meaning diabetic & nondiabetic moms) and each
> mom within
> group.  The nested ANOVA results are then shown.
>
>     . table mom group, c(mean y freq)
>
>     ------------------------------
>               |       group
>           mom |        1         2
>     ----------+-------------------
>             1 | 4.290885  5.353604
>               |       11         8
>               |
>             2 | 5.389515  6.748766
>               |        9         9
>               |
>             3 | 4.533185  6.990387
>               |        9        12
>               |
>             4 | 4.732775  5.633358
>               |        8        10
>               |
>             5 | 6.142506  4.981017
>               |       10         8
>               |
>             6 | 5.124625  5.217069
>               |        9        10
>               |
>             7 | 5.285787  5.418501
>               |       11        11
>               |
>             8 | 5.234306  6.799588
>               |       11         9
>               |
>             9 | 5.171794  6.379981
>               |        8         9
>               |
>            10 | 6.112025  6.664215
>               |       10         8
>     ------------------------------
>
>     . anova y group / mom|group /
>
>                  Number of obs =     190
> R-squared     =  0.1047
>                  Root MSE      = 2.40589     Adj
> R-squared =  0.0046
>
>         Source |  Partial SS    df       MS
>  F     Prob > F
>
>
-----------+----------------------------------------------------
>          Model |  115.021745    19  6.05377604
> 1.05     0.4119
>                |
>          group |   31.203949     1   31.203949
> 6.80     0.0178
>      mom|group |  82.6298147    18  4.59054526
>
>
-----------+----------------------------------------------------
>      mom|group |  82.6298147    18  4.59054526
> 0.79     0.7065
>                |
>       Residual |  984.009577   170  5.78829163
>
>
-----------+----------------------------------------------------
>          Total |  1099.03132   189  5.81498054
>
>
> I added a little group effect into my fake data
> (notice the
> p-value of .0178).
>
>
> Ken Higbee    khigbee@stata.com
> StataCorp     1-800-STATAPC
>
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=====
Ricardo
Statistician
Center for Applied Research
University Hospital
Oklahoma City,OK

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