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st: Re: clogit or logistic for matched pairs


From   Roger Newson <roger.newson@kcl.ac.uk>
To   statalist@hsphsun2.harvard.edu
Subject   st: Re: clogit or logistic for matched pairs
Date   Thu, 06 Nov 2003 16:34:01 +0000

At 17:01 06/11/03 +0100, Michael Ingre wrote:
Dear statalist

I want to estimate the risk for an exposed twin to develop a symptom
relative an unexposed twin. I have 169 pairs of monozygotic twins that are
discordant on my exposure variable. The basic design is a case (exposed) -
control (unexposed) study. The outcome variable (symptom) is binary coded.

The most obvious method should be -clogit- however, I'm not satisfied with
the statistics.

Like McNemar, it excludes the ties between cases and controls and the odds
ratio returned describes the ratio of cases "winning" over controls.
However, I would like to estimate the ratio of a case to HAVE the symptom
(not "winning") compared to the control.

I have been criticized for using logistic regression with the cluster option
and robust variance estimator. My OR is said to be invalid.

Actually, the criticism is harder than that: My analyses is said to be
"completely meaningless".

. logistic dv iv , cluster(twinpairid)

Do you have any advice?
I do not know your supervisor. However, if you want a risk ratio instead of an odds ratio, and you want to cluster the standard errors by twin pair, then you should use -glm- to fit a model with log link and binomial error. I would type

gene byte baseline=1
glm dv iv baseline, link(log) family(binomial) cluster(twinpairid) eform noconst

and the parameter -baseline- will be a risk for the unexposed twin, and the parameter -iv- will be a risk ratio.

The reason for defining the variable -baseline- and using the -noconst- option is that, otherwise, the -eform- option causes Stata not to print the intercept parameter -_cons-. We therefore need to define -baseline- as an X-variable and tell Stata that there is no intercept for it to hide from us.

I hope this helps.

Roger


--
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: roger.newson@kcl.ac.uk
Website: http://www.kcl-phs.org.uk/rogernewson

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

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