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Re: st: Equivalent to kruskal-wallis in clustered data


From   "Roger B. Newson" <r.newson@imperial.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Equivalent to kruskal-wallis in clustered data
Date   Thu, 08 Nov 2012 12:06:58 +0000

Sorry, I made a very stupid mistake in my last email. The -somersd- command should of course have been:

xi, noomit: somersd price i.rep78, transf(z) tdist cluster(firm)

so that the Somers' D parameters are estimated clustered by -firm-. We then type, as before:

testparm _I*

to do the F-test of the hypothesis that all Somers' D parameters are zero. The correct P-value is then 0.5973.

I hope this helps. Sorry for the confusing mistake.

Best wishes

Roger

Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/

Opinions expressed are those of the author, not of the institution.

On 08/11/2012 12:00, Roger B. Newson wrote:
Yes, there is a clustered version of the Kruskal-Wallis test. It can be
done using the -somersd- package (downloadable from SSC) with -xi:- and
-testparm-.

For instance, in the -auto- data, we might test independence of price
and repair record, assuming that we are sampling car firms from a
population of car firms, instead of sampling car models from a
population of car models. We set up the data by typing:

sysuse auto, clear
gene firm=word(make,1)
tab firm, m

This creates and tabulates  a new variable -firm-, indicating the firm
that makes each car model. We then do the analysis by typing:

xi, noomit: somersd price i.rep78, transf(z) tdist

which creates variables _Irep78_1 to _Irep78_5, indicating membership of
each of the 5 repair record groups, and calculates a Somers' D of each
of these indicators with respect to -price-, with confidence limits and
a P-value. These Somers' D parameters measure the association of each
repair record group (compared to all other repair record groups) with
the car's price in dollars.

To do the test, we then type:

testparm _I*

which tests the hypothesis that all 5 of these Somers' D parameters are
zero, which implies that no repair group tends to be more or less
expensive than the rest (the hypothesis usually tested using a
Kruskall-Wallis test). We see that the P-value is 0.5618, so the null
hypothesis has not been decisively refuted.

I hope this helps. Let me know if you have any further queries.

Best wishes

Roger


Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/


Opinions expressed are those of the author, not of the institution.

On 08/11/2012 10:11, alfonsa leiva wrote:
Dear fellows


Basically, GPs were randomized to 3 groups, in the bivariate analysis
of effectiveness dependent variable are continuos and independet
variable are groups 1,2 or 3 . There is any test equivalent to
kruskall-wallis implemented in stata to test the study hypothesis
adjusted for the lack of independency of the patients(clustered data
by GPs)?

Thanks in advance



Alfonso Leiva
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