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Re: Risks of low level radiation - New Scientist Article
> From: "Otto G. Raabe" <ograabe@UCDAVIS.EDU>
> At 09:22 PM 12/6/01 +0000, Jim Nelson wrote:
>> As I told Dr. Cohen a few weeks ago, I agree with the papers by Smith et al.
>> that describe the limitations of Dr. Cohen's work. The smoking data he uses
>> is so bad, it can only predict a little over 30% of the the lung cancers in
>> the counties. If there was no confounding, it should be able to predict 85%
>> or so. I do not call that good control of confounding.
> **************************************************************
> December 6, 2001
> Davis, CA
>
> Dear Jim:
>
> What do you mean "can only predict a little over 30% of the lung cancers"?
> Do you mean that a regression R2=0.3? If so, that's only a description of
> the fraction of the variability that is explained by the regression. The
> important thing would be whether the trend is statistically significant,
> not that there is considerable excess variability among the data. Such
> variability is to be expected in such a study.
>
> The key point that Prof. Cohen has shown so well is that the disagreement
> between LNT and the observations is extremely robust. It is observed no
> matter how you stratify the data. Just take Colorado as an example.
> Residents of Colorado annually receive among the highest lung doses in the
> U.S. from natural radon and its decay products in the air. Meanwhile,
> Colorado enjoys one of the lowest lung cancer rates in the nation. In 1995
> it was 49 the out of 51. Washington,DC, where radon concentrations are much
> lower, had the highest lung cancer rate in 1995 (Am. Cancer Society, 1996).
Very good synopsis! But...
> Of course, it is always possible to say that some yet-to-be discovered
> cross-level confounder could be causing the "apparent" disagreement with
> LNT, but it does seem unlikely.
Alvarez and Seiler correctly note that this isn't possible. What's a
"confounder?" A factor that affects a subset such that the results do not
represent the whole set. Bernie essentially measured the whole set. What
confounder can fix a "discrepancy" between the whole and itself? :-)
Regards, Jim
> Otto
>
> **********************************************
> Prof. Otto G. Raabe, Ph.D., CHP
> Center for Health & the Environment
> (Street Address: Bldg. 3792, Old Davis Road)
> University of California, Davis, CA 95616
> E-Mail: ograabe@ucdavis.edu
> Phone: (530) 752-7754 FAX: (530) 758-6140
> ***********************************************
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