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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