[ RadSafe ] Additional evidence for suppression of cancer by low-dose radiation

[Brian Riely]

The central limit theorem assumes an infinite number of independent and
identically distributed random variables


-----Original Message-----
From: radsafe-bounces at health.phys.iit.edu
[mailto:radsafe-bounces at health.phys.iit.edu] On Behalf Of Bill Prestwich
Sent: Tuesday, January 17, 2012 12:01 PM
To: 'The International Radiation Protection (Health Physics) Mailing List'
Subject: Re: [ RadSafe ] Additional evidence for suppression of cancer by
low-dose radiation

The central limit theorem does provide a mathematical justification of the
use of the normal distribution when the fluctuations result from many
independent factors.

Bill

-----Original Message-----
From: radsafe-bounces at agni.phys.iit.edu
[mailto:radsafe-bounces at agni.phys.iit.edu] On Behalf Of radiation
Sent: Monday, January 16, 2012 7:52 PM
To: radsafe at agni.phys.iit.edu
Subject: Re: [ RadSafe ] Additional evidence for suppression of cancer by
low-dose radiation

Maybe this is yet another case showing superficial use of statistics 
leading to possible misinformation. I am not trying to support any of 
the ideologies be it LNT or hormesis, I simply try to look at the 
article and look at the data provided and try to see whether some 
meaning can be derived.

The average concentration of indoor radon and gamma dose rates in Table 
1, if taken with 3 sigma (99.7%) rather than one (68.3%), shows that the 
variability of measurements is for each set a big fluffy cloud. 1/3 to 
1/4 of the population has died of cancer in a decade. The sources of 
cancer are multiple and some of the other causes (e.g. smoking) have 
been considered. However unemployment, education and economic status 
cannot be directly correlated with cancer rate.

In addition there is no law of nature or mathematics telling us that 
data distributions are Gaussian or "normal"; that's just the lazy way, 
requiring from the data analyst the least effort, Essentially here there 
is a simple correlation model: dose -- cancer rate; but cancer has so 
many more causes, so this correlation is in fact meaningless in such a 
context. With single parameter correlations in a complex world you can 
prove just anything and its contrary as well. Then look at the pictures 
and the variances on the two graphs and the corresponding table. If you 
take the straight line regression with fitting parameters confidence 
bounds of 3 sigma how the heck can you conclude from that, that there is 
a linear dependency? This leads to the conclusion that on the basis of 
the data it cannot be established whether a linear dependency is 
present: a visual inspection would suggest that there is no linear 
increase with increase of dose at low values.: All you can say: the data 
are surrounded by a large fog of uncertainty or noise which does not 
help reaching meaningful conclusions. Let's avoid proving once more that 
there are lies, big lies and statistics. In conclusion, according to my 
analysis, the data variability is such that a "decrease of all cancer 
death and lung cancer only" cannot be established. Let's be fair and 
stay away from ideology.

Dr. Enrico Sartori

> Hi All,
>
> A new Dose-Response Journal paper (pre-press version) by Krzysztof
Fornalski and Ludwik Dobrzynski provides additonal evidence for cancer
suppression via prolonged exposure to natural background radiation.  The
paper is titled "Cancer mortality in high natural background areas of
Poland" and can be freely accessed from the journal website:
http://dose-response.com/ .
>
> Best wishes,
> Bobby R. Scott
> Lovelace Respiratory Research Institute
> Albuquerque, NM, USA


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