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

Wed Jan 18 07:59:15 CST 2012

Bill is right about the distributions not having to be identical, However, this applies only when the distributions are ADDED. If the distributions are MULTIPLIED, not added, the resulting distribution is not normal. I have done this by using Monte Carlo sampling to multiply four normal distributions together, as follows

#1 - Mean = 1.0, Std Dev = 0.2
#2 - Mean = 0.9, Std Dev = 0.15
#3 - Mean = 1.1, Std Dev = 0.15
#4 - Mean = 1.0, Std Dev = 0.1

Although you can calculate the Mean of the product by multiplying the four means together, and the Standard Deviation by combining the relative standard deviations in quadrature, and these values agree with the Monte Carlo results, the resulting distribution is not normal. Rather, it is shaped more like a log-normal curve, extending further to the right than to the left. This becomes obvious if you consider two normal distributions, each with a mean of 1 and a standard deviation of 0.2. Multiplying (1 - 0.2) by (1 - 0.2) gives a value of 0.64, while (1+0.2) * (1+0.2) gives 1.44.

John Chase

-----Original Message-----
From: radsafe-bounces at agni.phys.iit.edu [mailto:radsafe-bounces at agni.phys.iit.edu] On Behalf Of Bill Prestwich
Sent: Tuesday, January 17, 2012 3:05 PM
To: 'The International Radiation Protection (Health Physics) Mailing List'
Subject: Re: [ RadSafe ] Additional evidence for suppression of cancer by low-dose radiation

First of all it becomes practically indistinguishable from the multiple convolution of independent distributions after some ten variables in all but pathological cases. Secondly not all the variables really have to have identical distributions.
Bill

-----Original Message-----
From: radsafe-bounces at agni.phys.iit.edu
[mailto:radsafe-bounces at agni.phys.iit.edu] On Behalf Of Brian Riely
Sent: Tuesday, January 17, 2012 2:53 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 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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