Re: Radon and Lung Cancer: What the studies really say.

[epirad@mchsi.com]

Dr. Cohen, below you state that your smoking data does a good job of predicting 

the county lung cancer rates. 



We stated before - 	



the results of this analysis do not support your claim that your smoking data 

accounts well for lung cancer rates in the counties.   We repeated the 

regression of lung cancer mortality rates on these adjusted smoking 

percentages.  The resulting R2 values indicate that S explains only 23.7% of 

the variation in lung cancer mortality rates among females and 34.5% among 

males.  The fit was not significantly improved when quadratic or cubic terms in 

S were added to the model.  Puntoni et al. (1995) compared six mathematical 

models relating cigarette smoking to lung cancer risk using data from nine 

large cohort studies.  They found that 67% of the variation in relative risks 

could be explained by a two-stage model of carcinogenesis.  In comparison, very 

few of the lung cancer deaths are explained by Cohen’s smoking variable.

	

One might argue that consideration of socioeconomic variables, for example, 

will help account for the rest of the variability in lung cancer rates.  To 

investigate this possibility, the 54 SEV used by Cohen were added to the 

regression of lung cancer on smoking.  The resulting R2 values were 37.0% for 

females and 50.6% for males.  Likewise, this adjustment falls short of 

predicting the lung cancer deaths.  It would appear that Cohen cannot offer a 

better control for these risk factors with the available data.



	The poor predictive power of S is due, in part, to a failure to allow 

for the effects of smoking intensity and duration.  To illustrate, consider two 

populations – one relatively younger than the other – that have the same 

percentage of smokers.  By modeling smoking as a dichotomous variable, one 

would estimate the risk from smoking to be the same in the two populations.  

This is counterintuitive since smokers in the older population are likely to 

have smoked for a greater length of time.  A variable such as pack-years would 

allow one to include effects of smoking duration.  Cohen’s failure to 

incorporate intensity and duration in his analysis naturally leads to a smoking 

variable which accounts for fewer lung cancer deaths.



Puskin's finding further supports that you smoking rates do a poor job of 

predicting lung cancers in counties since your inverse assocaition was found 

for other smoking related cancers which are not related to radon exposure.  

> 

> On Fri, 20 Jun 2003 epirad@mchsi.com wrote:

> 

> > Unfortunately the treatment described uses the same problematic smoking data

> > that does a poor job of predicting county lung cancer rates.

> 

> 	--The smoking data I use does an excellent job of predicting

> county lung cancer rates; it implies that all lung cancer is due to

> smoking; see section on "Defense of PITT S-values" in Item #15 on my web

> site. Your statement is based on its failure to do an excellent job in

> predicting all the small random fluctuations in smoking and lung cancer

> rates; this was explained in my paper in Health Physics 72:489-490;1997

> 

> 	--I have shown that no remotely plausible smoking prevalence data

> can explain the discrepancy between my data and LNT. See Sec. 4.2 of Item

> #7 on my web site.

> 



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