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

["Michael I. Lichter" <mlichter@buffalo.edu>]

Svend: The researcher who asked me about this likes the idea of 
reporting CIs and forgoing the explicit hypothesis test.

José: Unfortunately, it looks like the server ate the first line of your 
post so I'm not certain what you're suggesting. Is p(known) the 
pre-intervention proportion of adopters, is it an external estimate, or 
is it something else? If it's the first, I'm not sure I could justify 
that as a benchmark.

Thanks to both of you for your suggestions.

Michael

José Maria Pacheco de Souza wrote:
> bound p(known), test a hypothesis Ha: p(new)>p(known) vs H0: 
> p(new)=p(known), using the at risk of improving.
>    Or as Svend presented, just estimate the proportion of new, among 
> those at risk. In this case, aftward it will difficult to resist the 
> temptation to compare this result with the p(known).
> Cheers,
> José Maria
>
> Jose Maria Pacheco de Souza, Professor Titular (aposentado)
> Departamento de Epidemiologia/Faculdade de Saude Publica, USP
> Av. Dr. Arnaldo, 715
> 01246-904  -  S. Paulo/SP - Brasil
> fones (11)3061-7747; (11)3768-8612;(11)3714-2403
> www.fsp.usp.br/~jmpsouza
> -----
>
> > Michael asked and Joseph responded (not shown) - and Michael then wrote:
> >
> > 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.
> >
> > ===============================================================
> >
> > I wonder whether a P-value related to a somewhat arbitrary null 
> > hypothesis is useful. I think the following is more informative:
> >
> > Assume that you had 90 participants, 40 of whom already had the good 
> > habit, leaving 50 "at risk" for improvement. 20 (40%) of these 
> > improved. The 95% CI for this estimate is 26%-55%:
> >
> > . cii 50 20 , binomial
> >                                                         -- Binomial 
> > Exact --
> >    Variable |        Obs        Mean    Std. Err.       [95% Conf. 
> > Interval]
> > -------------+--------------------------------------------------------------- 
> >
> >             |         50          .4     .069282        .2640784 .548206
> >
> > Hope this helps
> > Svend
>
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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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