Re: Mechanisms are Needed to Explain Cohen's Data

[Kai Kaletsch <info@eic.nu>]

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

From: "BERNARD L COHEN" <blc+@PITT.EDU>

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

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

Sent: Thursday, January 10, 2002 9:53 AM

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





>

> On Tue, 8 Jan 2002, Kai Kaletsch wrote:

>

> > Of course, it is also interesting to test a better model than BEIR IV.

> > Therefore, we could also test a model that correlates lung cancer to

lung

> > dose. This is a separate issue.

>

> --See Sec.M of my 1995 paper.



All the models in Sec.M deal with a correlation of radon concentration and

lung cancer. One should correlate radon progeny exposure or lung dose to

lung cancer. I proposed a mechanism where the correlation between radon and

radon progeny as well as radon and lung dose is dependent on smoking status.



> I would be delighted to do my

> analysis with another model (eg BEIR-VI) if I could get it published. It

> would be a lot of wasted effort if I couldn't get it published. If someone

> would suggest this in a letter to the Editor, that would open the door for

> me to do the analysis and get it published, at least briefly.



Do you think that would convince anyone that is not already convinced that

LNT must go?



> > 1.    The first mechanism that I want to propose is the possibility of

an

> > incorrect smoking to radon relationship on the county level. Homes with

> > smokers have on average 0.9 times the radon concentration of homes where

no

> > one smoked.

> > It is not a trivial matter to take the known relationship that homes

with

> > smokers have on average 0.9 times the radon concentration of homes where

no

> > one smoked and move this to the county level. How was this done in the

> > analysis of Cohen's data?

>

> --See my forthcoming paper on "Treatment of confounding factors in

> an ecological study" which is posted on my web site

> www.phyast.pitt.edu/~blc

> This problem is treated in the latter part of Section D and in Table 4



Thank you for the specific citation, it sure beats wading through the

mountain of literature. The latter part of section D deals with smokers who

have less radon than non-smokers. What my post pointed out is that

non-smokers will have their radon systematically lowered by the presence of

smokers. I'm not sure that is treated in section D.



> > 2.    The second mechanism deals with the influence of smoking on the

> > Equilibrium factor F.

>

> --This is effectively a difference in radon exposure for smokers

> and non-smokers, which is treated in the reference above, Sec D and Table

> 4.



So, if we found a systematic mechanism, like the filter, that would cut F

for smokers to 0.5 of that of non-smokers, the best estimate of your value x

in table 4 would be 0.9 * 0.5 = 0.45. The lowest value listed in the table

is 0.8. (Looking at the table, it seems to move B in the wrong direction,

but I am having a bit of trouble getting my head around it.)



>  There are competing factors that affect the

> > Equilibrium factor in smokers' houses (increased ventilation reduces F,

> > increased aerosol concentrations increase F, air cleaners reduce F .). I

> > have no idea which one would be dominant

>

> --When considering air cleaners, it is vital to include their

> effect on unattached fraction. Air cleaners can easily reduce the WLM, but

> they also increase the unattached fraction and the two effects normally

> cancel each other fairly closely. That is why air cleaning is not

> generally regarded as a cheap and easy way to solve the radon problem.



I meant the use of filtration units that is causally related to smoking. I

think in most cases the combination of smoking and filtration will result in

a higher number of attachment sites than the combination of not smoking and

no filtration.



> > 3.    The third mechanism deals with the influence of smoking on the

number

> > of unattached radon progeny in the air. This becomes important if we

want to

> > test the data against an "improved" BEIR model, one that uses lung dose

as

> > the suspected carcinogen, rather than WLM. Unattached radon progeny are

said

> > to deliver larger lung doses than attached radon progeny.

>

> --Again this is essentially a difference between radon exposures

> for smokers and non smokers, covered in the above reference



Could it go outside the range of values covered in your Table 4? The values

covered in table 4 were reasonable values for radon values, not effective

radon values, adjusted for dose.



> > :) ] The point is that the mechanism is all-important. Every observation

> > must have an explanation, whether the observation is on an individual

level

> > or an aggregate level. If you are not looking for a mechanism, you are

not

> > doing science!

>

> --In any large data set such as mine (nearly a million data

> entries) there are innumerable correlations. I use these to give

> perspective on what sort of correlations are plausible. But I don't see

> why it is necessary to explain them. "Doing science" is largely testing

> theories, and if the test fails, it is only necessary to find a not

> implausible reason for the failure if the theory is to survive. In my

> studies, I have not been able to find such a not implausible explanation

> that could save LNT.



I don't see how one can judge the plausibility of the correlation, if one

does not consider the underlying mechanism. For example, you deal with

migration in your papers and conclude that it cannot explain the observed

radon - lung cancer relationship. Would that conclusion still hold if the

following mechanism was to be confirmed?:



High radon in the interior of the US causes precursor to lung cancer

conditions in people . These precursor conditions make the dust, pollen and

cold air of the interior unbearable and these people move to the coast,

where radon levels are low. There the precursor conditions develop into lung

cancer.



Plausibility is not just a statistical exercise. It also depends on the

plausibility of the underlying mechanism.



You have proposed a mechanism that can explain your data: The failure of

LNT. This is a perfectly good mechanism (even though it would be nice if we

had something to replace LNT with. Then we could test the plausibility of

this proposed mechanism against other data.) Mechanisms that explain your

data and are consistent with LNT should really be proposed by the people who

want to save LNT. Unfortunately, you can't force them. It might help your

argument to collect 20 or 30 of the most obvious mechanisms which could

introduce a bias and dispose of them either with a statistical argument or

with a logical argument.



I would be glad to write a letter to the Editor saying that these 20 or 30

mechanisms must be disposed.



Kai Kaletsch

http://www.eic.nu



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