[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]
Re: st: RE: Interobserver agreement for continuous variables
At 21:43 17/10/02 -0700, Nitin wrote:
I personally would use a variation on the intra-class Kendall's tau-a,
which can be calculated (with confidence limits) using my -somersd-
package, downloadable from SSC. (Type -ssc describe somersd- or -net search
somersd- within Stata to find how to download.) First, create a data set
with 1 obs for every ordered pair of measurements on the same subject by
different observers. Such a data set might have variables as follows:
Hi Nick and Scott,
Thank you for the response. But the concord and other
tests contained in it are only for 2 observers/raters.
I am looking for something that calculates
interobserver agreement for a continuous variable WHEN
THERE ARE MORE THAN 3 RATERS AND THE number of raters
vary for each subject ?
I look forward to hearing from you.
subjid Subject ID
obsid1 First observer ID
obsid2 Second observer ID
y1 First observer rating
y2 Second observer rating
Such a data set might be created using the -joinby- package. Note that each
combination of Observer A, Observer B and Patient X should appear twice in
this data set, once with Observer A as first observer and Observer B as
second observer, and once with Observer B as first observer and Observer A
as second observer. Once you have created this data set, type
somersd y1 y2,taua tdist cluster(subjid)
and Stata will calculate confidence limits for a Kendall tau-a between -y1-
and -y2-, clustered by -subjid-. This tau-a is a difference between 2
probabilities, namely the probability of concordance (two raters order two
subjects in the same way) and the probability of discordance (two raters
order two subjects in the opposite way). For instance, if Kendall's tau-a
is 0.70, then this means that, if 2 observers are given 2 randomly chosen
subjects and asked which one to rate more highly, then the observers are
70% more likely to agree than to disagree.
For more information on Kendall's tau-a, a possible reference is Newson
(2002), which in turn has many more references.
I hope this helps.
Newson R. Parameters behind "non-parametric" statistics: Kendall's tau,
Somers' D and median differences. The Stata Journal 2002; 2(1): 45-64.
Lecturer in Medical Statistics
Department of Public Health Sciences
King's College London
5th Floor, Capital House
42 Weston Street
London SE1 3QD
Tel: 020 7848 6648 International +44 20 7848 6648
Fax: 020 7848 6620 International +44 20 7848 6620
or 020 7848 6605 International +44 20 7848 6605
Opinions expressed are those of the author, not the institution.
* For searches and help try: