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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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