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Re: st: Analysis of proportion as the dependant variable


From   Ronan Conroy <rconroy@rcsi.ie>
To   "statalist hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: Analysis of proportion as the dependant variable
Date   Mon, 23 Jun 2003 10:25:10 +0100

on 22/06/2003 12:57 pm, Drora Fraser at fdrora@bgumail.bgu.ac.il wrote:

> We have data on proportion of children stunted at age 1 from 13
> locations, for each of 3 years for boys and girls.
The numbers of children in each group will vary, and it would be helpful to
take this into account. You can use -expand- to recreate a dataset with one
observation per child, by creating a variable that records the number of
children in each of the subcategories defined by sex, year, location and
stunting.

Be careful however. If there are NO children in a category, -expand- will
not create zero observations, it will simply not expand that observation.

> We wish to examine the association between stnting and hospitalization,
> at the level of the location, contrlling for year, sex and other
> factors.

If you use Stata's -svy- commands, you can set location as a stratum
variable, allowing the prevalence of stunting to differ between location.
This should give you a clear run at a simple logistic model. I would try
putting in dummies for year2 and year3.

> Which of the 3 is the correct analysis: 1. multivariable regression, 2.
> XT-GEE regression with (group variable=location, link=log,
> family=poisson) 3. Poisson regression.
> We would appreciate an answer or a link to anyone who might be able to
> help us.

I would be slow to analyse the proportions themselves. Each proportion is
probably based on a different sample size, and so has a different degree of
precision. Reflating the data to person-level data will take this into
account.


Ronan M Conroy (rconroy@rcsi.ie)
Lecturer in Biostatistics
Royal College of Surgeons
Dublin 2, Ireland
+353 1 402 2431 (fax 2764)

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