Re: st: Population Attributable Fraction/punaf: Diagnostics for Wide CIs

[Roger Newson <r.newson@imperial.ac.uk>]

Fat confidence intervals for PAFs are usually caused by fat confidence 
intervals for the corresponding population unattributable fraction 
(PUF), which in turn are caused by high standard errors for the linear 
combination of log odds and/or log odds ratios whose sampling 
variability determines the sampling variability of the PUF. These, in 
turn, may be caused by the fact that the kind of subjects in the fantasy 
scenarios invlved are not well reperesented in the real-world sample. 
For instance, if a sample contains very few non-smokers, then there will 
usually be a fat confidence interval for the difference that would be 
made to disease prevalences if the whole world stopped smoking.
I cannot comment any further on your case in particular, as I have not 
seen your data. However, a standard rule of thumb for logistic 
regression is to divide the number of events or non-events (whichever is 
smaller) by the number of parameters estimated, and check whether if 
this ratio is less than 5. If so, then you probably are trying to 
estimate too many parameters with your available data.
I hope this helps.

Best wishes

Roger


Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/

Opinions expressed are those of the author, not of the institution.

On 12/07/2011 09:54, graupel75@gmail.com wrote:
> Hello,
>
> I am using [punaf] to calculate the Population Attributable Fractions
> (PAF) from a logistic regression, and have checked models with
> Hosmer-L, AIC/BIC, and deltabeta residuals.
>
> I am getting very broad CIs eg, 22% (4 to 37%) for multiple risk
> factors and multiple related models.
>
> Are there any methods to determine if these ranges are due to
> inherently noisy survey data, or indicate simply inappropriate /
> unstable models?
>
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