[ RadSafe ] Re: "-authors do not report-" data refuting their conclusions!

[John Jacobus]

I am sorry you do not agree.  Maybe others some
insight into which of us is wrong with interpreting
this report.

I think it is very bold of you to say the authors
biased the data.  Do you have any proof of this expect
that you do not like their conclusions?  Did you see
the other paper, or do you think that data is also
biased?

--- howard long <hflong at pacbell.net> wrote:
> John, I disagree with your interpretation.
>  
> Prejudice is the authors. They set up data so that
> only harm and not benefit would show, as everything
> below a dose, column 1, Table 2.
>  
> Fortunately, the data in the rows isolates by  dose
> both above and below, allowing the clear 
> J curve of data, refuting LNT.
>  
> Howard 
> 
> John Jacobus <crispy_bird at yahoo.com> wrote:
> Again, the left column represents the data in that
> range of doses. The rows represent the extent of the
> analysis. It the bottom row of first section,
> considers the analysis of only two columns of data,
> 0
> and 0-9 rad. The trend test and value P shows how
> well the curve fits the data. As you go up the row,
> more data is used to develop the curve, and as you
> can
> see the test of the curve fit gets better through
> the
> whole range of data, even if it gets worst at the
> lower ranges. 
> 
> I do not know what you mean by the "one-tail test."
> I
> believe what you are referring to is the chi-squared
> test. However, I believe that they are using some
> other correlation test to evaluate the fitting of
> the
> dose to the cancer incident in table 2, which is not
> the same thing. The analysis of the fit results the
> P-value, which they discuss under "Results" on page
> 19. Since you used to study epidemiology, I assume
> you understand their analysis.
> 
> The point that I am making is that the observed
> cancers does not change in any one column. However,
> the expected value changes as a new curve is
> developed
> for the data used. In the bottom row of the first
> section, the expected and observed cancers are
> equal. 
> One could say that at the 0-9 rad dose levels, the
> LNT
> is a good predictor of radiation effects. As you add
> higher doses, the LNT is less so, which is not
> surprising as there is a lot of "noise" data based
> on
> the P value from the bottom row of the first
> section. 
> However, as I have mentioned, the expected is only a
> mathematical projection. While the actual number of
> cases may, as you say, not fit the LNT hypothesis,
> that is not be the complete story. For the lowest
> row
> in the first section, it does a very good job. 
> 
> If you want to look at the epidemiological data that
> compares expected and actual cancer in the
> population,
> you should look at the one page from McGregor and
> Land
> report of 1977 that I have attached. 
> 
> My point is that you have to be clear in what you
> say.
> The fact is that x numbers of cancers were observed,
> which is a fact. However, when you say expected in
> this study, the expected is based on intrapolation
> from the data. It is not epidemiological data which
> is what you should be comparing the observed with
> the
> expected. From the McGregor and Land paper of 1977,
> the attachment, it appears that there is no
> beneficial
> effect from doses of 0-9 rad. 
> 
> When you say that the number of observed is
> different
> from the number expected on the top line, I would
> ask
> what is the basis of you expected number? This is
> good science and not data mining.
> 
> -- John
> 
> --- howard long wrote:
> > John,
> > The left column, labeled "Dose range kerma",
> unlike
> > the rows, covers dosage from a given level to 0.
> > That is consistent with their one-tail test, which
> > assumes that any radiation under the specified
> > amount gives increased risk of cancer. 
> > 
> > Their own top rows of O and E do not fit that LNT
> > hypothesis.
> > 
> > Howard 
> > 
> > John Jacobus wrote:
> > The question is this.
> > As you look down the columns of table 2, the
> > observed
> > cases stay the same. However, the expected number
> of
> > cases change. I say that this is due to the use of
> > the data to draw linear curves thought the data
> > sets.
> > 
> > Do you agree or do you have another explanation?
> 
> 
> 
> 
> 
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+++++++++++++++++++
"A positive attitude may not solve all your problems, but it will annoy
enough people to make it worth the effort." Herm Albright

-- John
John Jacobus, MS
Certified Health Physicist
e-mail:  crispy_bird at yahoo.com


		
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