Re: Cohen's Fallacy

[Tom Mohaupt <tom.mohaupt@WRIGHT.EDU>]

Mr. Rad Health,

Ruth suggests that cases with smoking be subtracted from the data. You say

that that this is what was done with the Iowa study, and I disagree. The

cases with smokers were not subtracted, but included in the evaluation. 



Secondly, you espouse the virtues of a multivariate regression to account

for smoking and I disagree that this is adequate for radon and lung cancer.



For most cancers other than lung, smoking is not a direct insult to the

organ in question, i.e., smoking tends to be another confounding factor. In

lung cancer, smoking directly insults the lung. About 90% of the lung

cancers would go away if smoking were removed.  Smoking deposits hot

particulates in the lung. It contains enumerable chemical carcinogins.

Smoking directly changes the physiology of the respiratory system, thus

hindering the lung from functioning properly. It also deposits a higher

dose from alpha particles than from radon. Theory suggests that the alpha

particles from radon induce lung cancer. The dose from smoking is much

greater than radon, but it is not accounted for in the population-based

radon studies. It's not a simple confounder and cannot be treated as such.

Also, many of the radon studies of never-smokers have been negative. If the

smoking model is correct, the results of studies including smokers and

never-smokers should agree. Since they don't, I question whether the

modeling for smoking is correct.

Tom



Rad health wrote:

> 

> Ruth's second points:

> 



> 

> >6.  Look at whatever relationship exists between radon exposure and the

> >remaining lung cancers (those that occurred in non-smokers).   It is also

> >necessary to look at the secondary smoke exposure in non-smokers, since

> >secondary smoke is also a pretty well extablished carcinogen.

> 

> This ignores the interaction between smoking and radon.  You get alpha

> exposure from both radon and tobacco.

> This part of what Field did and this is likely what will be done in a

> pooling.

> >

> >Maybe this is what Field et al did -- I will have to get their papers and

> >see.  But it seems to me that applying some kind of statistical correction

> >for smoking that says, in effect, since the national risk of lung cancer

> >from

> >smoking is x, and the national (or statewide) fraction of smokers is y,

> >then

> >x*y *observed lung cancers are attributable to smoking and the rest to

> >radon,

> >doesn't really cut it.   All that would tell you is the fraction of lung

> >cancers that MIGHT be attributed to smoking.

> 

> Regarding a smoking correction - As posted before on this list by others,

> this is the most powerful way to adjust for smoking.

> 

> Quoting Hosmer and Lemeshow's authoritative book, "Applied Logistic

> Regression":

> 

> "One generally considers a multivariate analysis for a more comprehensive

> modeling of the data.  One goal of such an analysis is to statistically

> adjust the estimated effects of each variable in the model for differences

> in the DISTRIBUTION of and associations among the other independent

> variables.  Applying this concept to a multivariate logistic regression

> model, we may surmise that each estimated coefficient provides an estimate

> of the log odds [of lung cancer] adjusting for all other variables [smoking]

> included in the model."

> 

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-- 

Thomas Mohaupt, M.S., CHP

University Radiation Safety Officer



104 Health Sciences Bldg

Wright State University

Dayton, Ohio 45435

tom.mohaupt@wright.edu

(937) 775-2169

(937) 775-3761 (fax)



"An investment in knowledge gains the best interest." Ben Franklin

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