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RE: st: RE: Population Attributable Fraction (PAF) estimation

From   "Nick Cox" <[email protected]>
To   <[email protected]>
Subject   RE: st: RE: Population Attributable Fraction (PAF) estimation
Date   Fri, 6 May 2005 17:37:07 +0100

It is for StataCorp to comment on future plans, not me. 

As often discussed and explained on this list, 
StataCorp tend not to go into tremendous detail
on their plans. Sometimes they do say, "This 
will be added shortly", especially in respect 
of easy small fixes or extras, or "This is not on 
our current to do list", meaning if you wait 
for this to be done, you might wait for ever. 

One way of lobbying for future changes is 
through Statalist, so you just did it. Again, 
StataCorp tend to listen but not speak. Among
other things, they note the degree of support 
(including silences). Of course, there are other 
criteria, including the amount of user activity 
in an area. 

Among many other issues, there are hundreds 
and hundreds of methods that are important 
to groups of users. It is just not possible 
for StataCorp to implement more than a few. On the face of 
it, this one is a technique that is user-programmable; 
there are just happen to be some problems with 
the existing program so far as you are concerned. 
In any major area the usual consequence of that 
is a flurry of alternative programs and/or 
user complaints. 

Also, what are especially problematic for StataCorp to 
implement are contentious areas in which even 
the supposed experts disagree. 

These are broad comments, as I am not familiar
at all with this field. 

[email protected] 

roger webb

> Thanks for your response. Do you know if there are any plans to 
> implement an adjusted PAF estimator in future versions of Stata?
> If not, is there a means of lobbying for implementation of such a 
> routine in 'official Stata'?
> Rockhill et al (Am J Public Health 1998; 88: 15-19) comment that 
> errors in PAF estimation are common in published papers due to 
> confusion over appropriate use of formulae for different types of 
> data, especially in relation to valid estimation with covariate 
> adjustment. Implementation of a valid and flexible post-regression 
> estimator would surely address these problems. 

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