I assume "row" means "leg" or "limb".
I don't think this is a Stata issue,
as Stata's behaviour appears correct
given the data and what you ask of it.
Please explain why you think your
by-hand analysis is correct. I
don't think you can collapse the
table in this way. A test of
that is that if you reverse the
table you cannot recover even a subset
of the data correctly, as you do not have
complete data on 6 limbs. Rather, you have
complete data on 5 limbs only,
as Stata is telling you.
Nick
[email protected]
Louise Linsell
> I have a dataset that looks something like this - each person has
> observations on 2 hips and 2 knees and I wish to calcuate
> the odds ratio
> of hips to knees, taking account of the clustering within
> person using
> robust standard errors.
>
> id hip knee row
> 1 1 0 1
> 1 1 0 2 (1=replaced, 2=not replaced)
> 2 . 1 3
> 2 0 1 4
> 3 1 0 5
> 3 0 . 6
> 4 1 0 7
> 4 . . 8
>
> I have tried all of the following commands
>
> logistic hip knee, cluster(id)
> xtlogit hip knee, i(id) or
> svymean hip, by(knee)
>
> but all of them drop any record (line) with a missing value
> (.), so in
> the above dataset, rows 3, 6 and 8 would be dropped,
> despite there being
> a non-missing observation in the other group that should be
> included in
> the analysis. This gives incorrect estimates for the odds
> ratio when you
> calculate them by hand from the equivalent 2x2 table. e.g from the
> above example, if I was calculating the OR by hand I would get
>
> yes (1) no (0)
>
> hips 4 2
>
> knees 2 4
>
> OR = (4X4)/(2X2) = 4
>
> However if all the rows with the missing values are dropped, you get
>
> yes (1) no (0)
>
> hips 4 1
>
> knees 1 4
>
> OR = (4X4)/(1X1) = 16
>
> Any ideas or suggestions on how to calculate the correct
> odds ratio and
> robust s.e. would be much appreciated.
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