Ronan, thank you for your suggestion.
My understanding of -kapci- is that it treats all observations as coming from one sample and selects random observations with replacement from this one sample. This is in contrast to there being 6 'clusters' or 'strata' of observations that make up a sample. The -bootstrap- command of Stata allows sampling by either 'cluster' or 'strata', I am just unsure as to which is more appropriate to the situation of say 6 medical clinics, with one observer assesing two methods of measuring a binary outcome on each patient and calculating kappa.
bootstrap k=r(kappa) , cluster(clinic) rep(1000) : kap a b
or
bootstrap k=r(kappa) , strata(clinic) rep(1000) : kap a b
A similar situation occurs when applying McNemar's test to compare the two paired proportions over the 6 'clusters' or 'strata'.
Best wishes, Garry
________________________________
From: [email protected] on behalf of Ronan Conroy
Sent: Fri 10/12/2007 20:44
To: [email protected]
Subject: Re: st: kappa and bootstrap
On 11 Oct 2007, at 23:12, Garry Anderson wrote:
> Dear Statalist,
>
> I am calculating kappa over six clusters, which range in sample
> size from 30 to 150.
> I wish to adjust the 95% confidence intervals for possible lack of
> independence of observations.
> The clusters could be patients within medical clinics.
>
> I was enquiring which of either the cluster option or the strata
> option of the bootstrap command would be appropriate.
You might consider using -kapci- written by Michael E. Reichenheim
and downloadable from ssc.
P Before printing, think about the environment
=================================
Ronan Conroy
[email protected]
Royal College of Surgeons in Ireland
Epidemiology Department,
120 St Stephen's Green, Dublin 2, Ireland
+353 (0)1 402 2431
+353 (0)87 799 97 95
http://www.flickr.com/photos/ronanconroy/sets/72157601895416740/
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