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

["Michael I. Lichter" <MLichter@Buffalo.EDU>]

Joseph,

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.

Thanks.

Michael


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. <mlichter@buffalo.edu>
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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