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

From   "Michael I. Lichter" <MLichter@Buffalo.EDU>
Subject   Re: st: Re: single sample pre/post comparison of proportions
Date   Wed, 10 Jun 2009 23:27:36 -0400


Thanks for the quick & reasonable response.

The suggestion of a one-sample test restricted to pre-intervention ADOPT=NO crowd makes sense. I think you are also sneakily suggesting that the most obvious null hypothesis -- "H0: p = 0" is not a good choice; there would probably be some adoption even in the absence of the intervention, and the intervention probably cannot be called a success unless the proportion of adopters exceeds a minimum cost/benefit threshold. Instead, I could choose, e.g., "H0: p < .25" (a one-tailed test). That seems reasonable.



Joseph Coveney wrote:
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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Michael I. Lichter, Ph.D. <>
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 125 / Phone: 716-898-4751 / FAX: 716-898-3536

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