# st: Re: Population attributable percentage

 From Jay Kaufman <[email protected]> To [email protected] Subject st: Re: Population attributable percentage Date Wed, 22 Oct 2003 09:27:46 -0400

```Anthony Gichangi wrote:
>
> There exist several ways to compute the 95 % CI for the populatioon
> attributable risk depending
> on the type of study. A good reference is .
>
> Comparisons of Confidence Intervals for Attributable Risk
> Hoi M. Leung; Lawrence L. Kupper
> Biometrics, Vol. 37, No. 2. (Jun., 1981), pp. 293-302.

This article also contains a method that can be readily applied
to the PAR%, as demonstrated in their examples:

Am J Epidemiol. 1998 Apr 15;147(8):783-90.
Confidence limits made easy: interval estimation using a substitution method.
Daly LE.

Abstract:

The use of confidence intervals has become standard in the presentation of
statistical results in medical journals. Calculation of
confidence limits can be straightforward using the normal approximation with an
estimate of the standard error, and in particular cases
exact solutions can be obtained from published tables. However, for a number of
commonly used measures in epidemiology and
clinical research, formulae either are not available or are so complex that
calculation is tedious. The author describes how an approach
to confidence interval estimation which has been used in certain specific
instances can be generalized to obtain a simple and easily
understood method that has wide applicability. The technique is applicable as
long as the measure for which a confidence interval is
required can be expressed as a monotonic function of a single parameter for
which the confidence limits are available. These known
confidence limits are substituted into the expression for the measure--giving
the required interval. This approach makes fewer
distributional assumptions than the use of the normal approximation and can be
more accurate. The author illustrates his technique by
calculating confidence intervals for Levin's attributable risk, some measures in
population genetics, and the "number needed to be
treated" in a clinical trial. Hitherto the calculation of confidence intervals
for these measures was quite problematic. The substitution
method can provide a practical alternative to the use of complex formulae when
performing interval estimation, and even in simpler
situations it has major advantages.

--
Jay S. Kaufman, Ph.D
-----------------------------
email: [email protected]
-----------------------------
Department of Epidemiology
UNC School of Public Health
2104C McGavran-Greenberg Hall
Chapel Hill, NC 27599-7435
phone:  919-966-7435
fax:    919-966-2089
-----------------------------
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