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Re: Cohen's Ecologic Studies
Your postings usually seek to discredit the Cohen study, but they
usually fall short of being understandable. If you have a clear and
valid point to make, please do so. Its no good saying there is a
problem without showing clearly what the problem is.
So how about it? Since you are determined to discredit the study on
Radsafe, make your case clear. Respond to the question:
------------------------------------------------------
--Why can't you make up a specific numerical example and show
how
it can affect my results?
------------------------------------------------------
If you can't do that, then I agree with you, "there is no point to
continue this dialog" and you should stop gnawing this bone.
_______________________________________________
Gary Isenhower
713-798-8353
garyi@bcm.tmc.edu
epirad@mchsi.com wrote:
>
> Dr. Cohen,
>
> It is clear you do not understand the points I am trying
> to make. If you really believe that non linearity of
> confounding has nothing to do with your findings than
> there is no point to continue this dialogue. After all,
> it is not the first time we agreed to diagree. I think
> we can both agree on that.
>
> Lubin has recently given you an example of the problem,
> there is no need for me to repeat it.
>
> J. Radiol. Prot. 22 (June 2002) 141-148
>
> The potential for bias in Cohen's ecological analysis of
> lung cancer and residential radon
>
> Jay H Lubin
>
> Biostatistics Branch, Division of Cancer Epidemiology
> and Genetics, National Cancer Institute, EPS/8042, 6120
> Executive Blvd, Rockville, MD 20892-7244, USA
>
> Abstract. Cohen's ecological analysis of US lung cancer
> mortality rates and mean county radon concentration
> shows decreasing mortality rates with increasing radon
> concentration (Cohen 1995 Health Phys. 68 157-74). The
> results prompted his rejection of the linear-no-
> threshold (LNT) model for radon and lung cancer.
> Although several authors have demonstrated that risk
> patterns in ecological analyses provide no inferential
> value for assessment of risk to individuals, Cohen
> advances two arguments in a recent response to Darby and
> Doll (2000 J. Radiol. Prot. 20 221-2) who suggest
> Cohen's results are and will always be burdened by the
> ecological fallacy. Cohen asserts that the ecological
> fallacy does not apply when testing the LNT model, for
> which average exposure determines average risk, and that
> the influence of confounding factors is obviated by the
> use of large numbers of stratification variables. These
> assertions are erroneous. Average dose determines
> average risk only for models which are linear in all
> covariates, in which case ecological analyses are valid.
> However, lung cancer risk and radon exposure, while
> linear in the relative risk, are not linearly related to
> the scale of absolute risk, and thus Cohen's rejection
> of the LNT model is based on a false premise of
> linearity. In addition, it is demonstrated that the
> deleterious association for radon and lung cancer
> observed in residential and miner studies is consistent
> with negative trends from ecological studies, of the
> type described by Cohen.
>
> URL: stacks.iop.org/0952-4746/22/141
>
> Regards, Bill Field
> >
> > On Tue, 4 Jun 2002 epirad@mchsi.com wrote:
> >
> > > Dr. Cohen,
> > >
> > > To ignore non-linearity is the root cause of your
> > > findings.
> >
> > --Linearity of confounding factors has no relevance to my study
> >
> > In an ecologic analysis you are limited to a
> > > summary statistics to adjust for confounding. Since you
> > > do not have information on covariates at the county
> > > level, accurate ratio functions cannot be calculated.
> >
> > --Why can't you make up a specific numerical example?
> >
> >
> > > This becomes very problematic if the data structure is
> > > non linear (e.g. not everyone in the county smokes
> > > cigarettes for the same duration and intensity; not
> > > everyone spends the same amount of time in their home,
> > > not everyone is exposed to the same radon concentration,
> > > etc...), and non additive, which is the case at hand.
> >
> > --Why can't you make up a specific numerical example and show how
> > it can affect my results?
> >
> > >
> > > I believe the onus is on you to show that multiple non-
> > > linear covariates are not the cause of your problem.
> > > The only way I know you can attempt to do this is use
> > > the methods of Sheppard and colleagues.
> > >
> >
> > --I never assume anything is linear, except lung cancer vs radon.
> > I need a numerical example to understand what you are talking about.
> >
> > > Lubin has demonstrated the problem in a recent paper
> > > just using smoking.
> >
> > --My papers give examples of how errors in smoking can explain my
> > results, but then I show that the required correlations are completely
> > implausible. Lubin never addresses the issue of plausibility.
> >
> > Your inverse associations are found
> > > for other smoking related cancers that should not be
> > > related to radon. This further strengthens my argument
> > > that your inability to adjust adequately for smoking is
> > > driving your findings. Or do you believe the reason the
> > > other smoking related cancers also have an inverse
> > > association with your radon concentrations is because of
> > > a hormetic response due to alpha radiation exposure to
> > > the lung?
> >
> > --I have addressed this in previous messages
> >
> > I find it far more credible that the
> > > explanation is lack of control of confounding by smoking
> > > and other factors as Lubin has just demonstrated.
> > >
> > --In BEIR-IV, smoking is not a confounder. Smokers and non-smokers
> > are treated as entirely different species.
> >
> >
> > > You 1997a) claim that simple linear least squares
> > > regression of m on S indicates that nearly all lung
> > > cancer is due to smoking. However, the results of this
> > > analysis do not support such a claim. We repeated the
> > > regression of lung cancer mortality rates on your
> > > adjusted smoking percentages. The resulting R2 values
> > > indicated that S explains only 23.7% of the variation in
> > > lung cancer mortality rates among females and 34.5%
> > > among males. 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 your smoking variable.
> > > Therefore, the smoking variable is inadequate to adjust
> > > for the effects of smoking.
> > >
> > --I have addressed the R-squared issue previously and shown that
> > it is irrelevant
> > --I have also shown that any choices of the smoking prevalences in
> > the various counties that are not completely implausible would not affect my
> > results.
> >
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