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# RE: st:Confidence interval of difference between two proportions and -csi-

 From "Nick Cox" To Subject RE: st:Confidence interval of difference between two proportions and -csi- Date Fri, 19 Mar 2004 15:22:49 -0000

```The terminology "exact" is indeed used in this way, and there's
scarcely a chance of changing that terminology.

But as a matter of ordinary English it's potentially highly
misleading term for anyone who prefers that (for example)
95% means precisely that. I guess for everyone who's ingested
this explanation there are many more who think in terms of
coverage (without necessarily using that term).

Nick
n.j.cox@durham.ac.uk

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Dupont,
> William
> Sent: 19 March 2004 15:09
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st:Confidence interval of difference between two
> proportions and -csi-
>
>
> Statalisters
>
> I believe that there is some confusion about the meaning of exact
> confidence intervals.  Confidence intervals are defined in two ways.
> Let theta be a parameter and L  U be two statistics.  Then confidence
> intervals are defined as follows:
>
> Coverage definition:
>
> (L, U) is a 95% confidence interval for theta if Pr[L < theta < U] =
> 0.95
>
> Non-rejection definition:
>
> A 95% confidence interval, (L, U), consists of all values of
> theta that
> can not be rejected at the 5% significance level given the data.
>
> These two definitions are equivalent for normally distributed data in
> which the mean and variance are unrelated.  In epidemiology and other
> disciplines we often work with statistics (e.g. odds ratios) in which
> these definitions yield different intervals.  Exact
> confidence intervals
> use the non-rejection definition.  When estimating odds
> ratios from 2x2
> tables,  the total number of successes in both groups is
> close to being
> an ancillary statistic in the sense that knowing this total tells us
> nothing about the true odds ratio. The Conditionality
> Principle requires
> that we condition our inferences on ancillary statistics.  It is for
> this reason that we condition on the marginal totals of a 2x2
> table when
> making inferences about odds ratios.
>
> If you accept the conditionality argument then the usual exact
> confidence interval is correctly derived from the hypergeometric
> distribution.  It is an exact interval not because it uses the
> hypergeometric distribution but because it complies with the
> non-rejection definition given above.  It should be noted that when
> these definitions disagree, the non-rejection confidence interval will
> have a higher coverage probability than the analogous
> interval obtained
> by the coverage definition.   In this sense, it is a more conservative
> interval.
>
> See Rothman and Greenland (1998) for further details.
>

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