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RE: Mechanisms are Needed to Explain Cohen's Data



Ken Bogen addressed the biological mechanisms, and showed that EPA's

environmental radon data by county for the US vs. women lung cancer

mortality 1950-54 (smoking prevalence 4% and 11% in two age groups)

confirms Cohen's data showing beneficial results at low doses. See,

e.g.:

http://www.belleonline.com/n3v72.html

 

See the two paragraphs under "Materials and Methods," and Fig 2a!



Another of the many substantial studies that confirm Cohen's results. 



You have recommended that the LNT-defenders consider mechanisms to

support their position, but the very best of the science establishment

have spent a decade trying, and failing, to produce any plausible

mechanism to make the LNT theory "explain" the factual observations,

without just rationalizations parroted here, and producing poor science

and obfuscated data to support the LNT. 

 

Regards, Jim Muckerheide

 



-----Original Message-----

From:	Kai Kaletsch

Sent:	Fri 11-Jan-02 2:21 PM

To:	BERNARD L COHEN

Cc:	RadSafe

Subject:	Re: Mechanisms are Needed to Explain Cohen's Data





----- Original Message -----

From: "BERNARD L COHEN" <blc+@pitt.edu>

To: "Kai Kaletsch" <info@eic.nu>

Cc: "RadSafe" <radsafe@list.vanderbilt.edu>

Sent: Friday, January 11, 2002 11:09 AM

Subject: Re: Mechanisms are Needed to Explain Cohen's Data



> > > In case-control studies, they don't ask whether there are other

> > > people in the house who smoked.

> >

> > They don't have to. They actually measure the radon concentration of

the

> > house and assign it to the occupants.

>

> --I thought the problem you raised was that the radon progeny are

> different for smokers and non-smokers.



3 separate mechanisms were proposed (all related to smoking): 1. The

presence of smokers systematically decreasing radon gas levels for

non-smokers. 2. The effect of smoking on the conversion between radon

gas

and WLM. 3. The effect of smoking on the conversion between radon gas

and

lung dose. (Numbers 2 and 3 deal with the conversion between a measured

quantity and a quantity of interest.)



> > You are trying to use the total amount

> > of radon in the county and divide it up between smokers and

non-smokers.

>

> --I am not trying to divide it up. I normally assume the same

> average radon levels for smokers and non-smokers. In special

treatments, I

> assume there to be differences and investigate the consequences.



It is the special treatments that I was referring to (latter part of

Section

D in your paper on treatment of confounding factors). I think these

special

treatments are necessary for any study that tries to investigate the

link

between lung cancer and anything else. Especially, if the "anything

else" is

systematically related to smoking.



> --My treatments of plausibility of correlation are based on the

> assumption that there is no strong direct mechanism,



If everything but radon concentration and lung cancer is random and if

your

sample is big enough, everything "comes out in the wash". The question

is

what is a "strong" direct mechanism. The argument being made by others

is

that small systematic mechanisms (they call them biases) can have a

large

effect on the shape of your graph. My position is that they must

identify,

or at least propose, those mechanisms before I buy the argument.



Best Regards,

Kai Kaletsch



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