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st: Re: Nonparametric two way ANOVA

From   Roger Newson <[email protected]>
To   [email protected]
Subject   st: Re: Nonparametric two way ANOVA
Date   Fri, 06 Feb 2004 11:49:22 +0000

At 19:56 05/02/04 -0500, you wrote:
>Joseph doesn't specify whether the really interesting predictor is race >or
age group, or whether race is in any way ordinal (including binary eg >1 for
"Black" and 2 for "White"). However, assuming that race is a boring
>categorical variable and age group is an interesting ordinal variable,
>then Joseph might calculate a Somers' D for the association of viral >load
with age for each race, and then meta-analyse these Somers' D
>values for each race to create a grand mean Somers' D measuring the
>ability of higher age to predict higher viral load within races.

>More information on the -somersd- and -parmest- packages is available on
>my website (see my signature). Somers' D and Kendall's tau-a are >detailed
in my Stata Journal paper (Newson, 2002), which is available in
>pre-publication draft form on my website. (As is my Stata tip about
>calculating geometric means using the -eform()- option of -regress-.)

>I hope this helps. I would be able to make more specific suggestions if I
>knew more about precisely what association Joseph is trying to measure.

I should've specified - race is, in fact, a categorical variable.  I am
interested in the effect of age on viral load by race.

Could you explain what you mean by "meta-analyse" in terms of using STATA?
Meta-analysis in Stata is documented in the manuals under -[R] meta-. If you install the program -meta-, written by Stephen Sharp and Jonathan Sterne and downloadable using

findit meta

then you will be able to use -parmby- and -meta- together. For instance, you might want to get a pooled Somers' D of viral load with respect to age:

parmby "somersd age vload", by(race) label norestore format(estimate min95 max95 %8.2f p %8.2e)
list race parm label estimate min95 max95 p
meta estimate stderr

The first line will create in the memory a new data set, overwriting the old data set. The new data set has 1 observation per value of -race- and data on the Somers' D estimates, standard errors, confidence intervals and P-values. The second line will list the confidence intervals for Somers' D within each race, indicating the power of age as an ordinal predictor of viral load within each race. The third line creates a summary confidence interval for a pooled Somers' D between all races (visible on the first line of output). There are many other possibilities, eg measuring Kendall's tau-a instead of Somers' D.

I hope this helps.


Roger Newson
Lecturer in Medical Statistics
Department of Public Health Sciences
King's College London
5th Floor, Capital House
42 Weston Street
London SE1 3QD
United Kingdom

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
Email: [email protected]

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

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