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st: Re: single sample pre/post comparison of proportions


From   "Joseph Coveney" <jcoveney@bigplanet.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Re: single sample pre/post comparison of proportions
Date   Thu, 11 Jun 2009 11:58:42 +0900

Michael I. Lichter wrote:
Greetings! I was asked this afternoon about the most appropriate way to 
test of significance for a comparison of proportions before and after an 
intervention within a single population, given that the underlying 
dichotomous variable can only change from NO to YES and not from YES to 
NO (so that the proportion can only increase from pre to post). I wasn't 
sure about the answer ...

Is a two-sample test of proportions as in -prtest- reasonable under 
these circumstances, given a reasonable N? (The actual N=270, which 
should be OK for -prtest-'s asymptotic statistics, I think.) I think 
McNemar's test and Cochran's test are inappropriate (like the chi-square 
test) because they are ultimately tabular and there cell where pre=YES 
and post=NO is empty.

The specifics are that a group of physicians was presented with an 
educational intervention intended to prompt adoption of a set of 
behaviors. Some physicians had already adopted those behaviors, so their 
pre status (ADOPT = YES) did not change after the intervention; only 
physicians who had not adopted at the pre-test could change adoption 
status after the intervention.

There must be some fairly standard way of handling this in epidemiology 
where for some conditions people can go from disease-free to diseased, 
but not from diseased to disease-free.

--------------------------------------------------------------------------------

I don't know whether there is a standard way in epidemiology, but one approach
that comes to mind is first to determine your null hypothesis (the hard part),
and then use a one-sample test to evaluate whether the observed proportion of
adoptees (restrict analysis to those physicians whose baseline status is ADOPT =
NO) is different from your hypothesized proportion under the null.

Joseph Coveney



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