4 ms·

Note: the title is misleading and contrary to the authors' conclusion. Where are the moderators? "The association between cumulative dose, lagged 10 years, and

Note: the title is misleading and contrary to the authors' conclusion. Where are the moderators? "The association between cumulative dose, lagged 10 years, and solid cancer mortality was reasonably well described by a linear model (fig 1); inclusion of a parameter describing the linear association between cumulative dose and solid cancer contributed substantially to model goodness of fit (supplementary table B). The addition of a parameter for the square of cumulative dose led to only a modest improvement in model goodness of fit compared with the linear model (likelihood ratio test =2.51, df=1; P=0.11), suggesting some downward curvature (that is, a negative estimated coefficient for the quadratic term). The addition of a parameter for an exponential term in the model led to a modest improvement in model goodness of fit for a linear-exponential model compared with the linear model (likelihood ratio test =3.17, df=1; P=0.08), again suggesting some downward curvature. To assess the trend over the lower cumulative dose range, we estimated associations between cumulative dose and solid cancer mortality over restricted ranges of 0-400 mGy cumulative dose (excess relative rate 0.63 (0.34 to 0.92) per Gy), 0-200 mGy cumulative dose (0.97 (0.55 to 1.39) per Gy), 0-100 mGy cumulative dose (1.12 (0.45 to 1.80) per Gy), 0-50 mGy cumulative dose (1.38 (0.20 to 2.60) per Gy), and 0-20 mGy cumulative dose (1.30 (−1.33 to 4.06) per Gy) (supplementary table C). Over the restricted range of 0-200 mGy cumulative dose, the association between cumulative dose and solid cancer mortality was well described by a linear model, and the addition of a parameter for the square of cumulative dose led to minimal improvement in model goodness of fit compared with the linear model (likelihood ratio test=0.54, df=1; P=0.46)." They say over and over that linear model (with relative risk = 1 at 0 dose) is a good fit! this is LNT. They say the improvements of adding the other fit are marginal. The error bounds here are large, and the deviation is small but apparently systematic or real below 200mGy. The authors, as far as I can tell, are just reporting the figures without claiming LNT is wrong.

VikingCoder 3y ago
Um, I don't think the HN title is contrary... Quoting: > What this study adds > The results of an updated study of nuclear workers in France, the UK, and the US suggest a linear increase in the relative rate of cancer with increasing exposure to radiation > Some evidence suggested a steeper slope for the dose-response association at lower doses than over the full dose range > The risk per unit of radiation dose for solid cancer was larger in analyses restricted to the low dose range (0-100 mGy) and to workers hired in the more recent years of operations
- omgJustTest 3y ago
  The HN title is "Low dose radiation cancer 2x worse than predicted" than LNT for some unspecified region of the curve... they do not claim LNT is wrong, they do not even present their more sophistocated fits and they do not quote the 2x figure. It is misleading. The paper presents an LNT fit If you want to say "there is a bias above linear-no-threshold in the region less than 200mGy" you are correct, but also say that the bias has large error bars associated with it and that the data may or may not fit that trend. Unless you have p-values to back up the 2x claim, it shouldn't be in the title.
gwillen 3y ago
I'm not a statistician, but I think there's a bit in your excerpt that is actually a concerning display of poor statistical literacy. If you're fitting a function which grows asymptotically (i.e. is monotonically increasing at least past a certain point), the best (polynomial) fit absolutely cannot have a negative quadratic as the leading term. If your model gives one, it is 100% guaranteed to be an artifact. Treating it as "suggesting some downward curvature" is a pretty bad misunderstanding. If you have doubts about this, consider what would happen if we added datapoints at higher doses. Every single datapoint we add to the right side of the graph will make the fit of a negative quadratic significantly worse. Ultimately, if you continue the graph indefinitely to the right, the fit of a negative quadratic is guaranteed to be infinitely bad. Any hint to the contrary is inherently an artifact of the limited dataset. (It may well be the case that, under certain conditions with a range-restricted dataset like this, such a finding might indeed be more likely if the true function has some downward curvature. But that's not statistics, it's voodoo. All the associated statistical parameters, p-value, likelihood ratio, etc., are absolutely meaningless nonsense.)
- deleted 3y ago
  [deleted]
- omgJustTest 3y ago
  I think that in this case it is definitely a case of analytic-cotorsion. There is _heated_ and mostly opinionated debate about the low-dose regime. Briefly some people believe low-doses of radiation have a threshold (that one could get a certain amount before having excess cancer risk), there is "no-threshold" meaning relative risk = 1 and dose = 0, and there are people who say that some radiation is good for us because it stimulates our repair mechanisms and kills weak cells. All of this heated debate brings us to the currently misunderstood findings, and is why the author's say over-and-over that the linear model is a good fit. People, specifically the regulators and the nuclear industry, make a _bunch_ of health decisions on excess risk based on Linear-No-Threshold (LNT) models because they believe it is conservative. The main issue here is that in order to get good statistics, you have to irradiate people, and the two best datasets prior to today are Hiroshima survivors and the small number of low-dose exposures. People are torturing this analysis to resolve the debate, which in my opinion only feeds it.

VikingCoder 3y ago

Um, I don't think the HN title is contrary... Quoting: > What this study adds > The results of an updated study of nuclear workers in France, the UK, and the US suggest a linear increase in the relative rate of cancer with increasing exposure to radiation > Some evidence suggested a steeper slope for the dose-response association at lower doses than over the full dose range > The risk per unit of radiation dose for solid cancer was larger in analyses restricted to the low dose range (0-100 mGy) and to workers hired in the more recent years of operations

omgJustTest 3y ago
The HN title is "Low dose radiation cancer 2x worse than predicted" than LNT for some unspecified region of the curve... they do not claim LNT is wrong, they do not even present their more sophistocated fits and they do not quote the 2x figure. It is misleading. The paper presents an LNT fit If you want to say "there is a bias above linear-no-threshold in the region less than 200mGy" you are correct, but also say that the bias has large error bars associated with it and that the data may or may not fit that trend. Unless you have p-values to back up the 2x claim, it shouldn't be in the title.

gwillen 3y ago

I'm not a statistician, but I think there's a bit in your excerpt that is actually a concerning display of poor statistical literacy. If you're fitting a function which grows asymptotically (i.e. is monotonically increasing at least past a certain point), the best (polynomial) fit absolutely cannot have a negative quadratic as the leading term. If your model gives one, it is 100% guaranteed to be an artifact. Treating it as "suggesting some downward curvature" is a pretty bad misunderstanding. If you have doubts about this, consider what would happen if we added datapoints at higher doses. Every single datapoint we add to the right side of the graph will make the fit of a negative quadratic significantly worse. Ultimately, if you continue the graph indefinitely to the right, the fit of a negative quadratic is guaranteed to be infinitely bad. Any hint to the contrary is inherently an artifact of the limited dataset. (It may well be the case that, under certain conditions with a range-restricted dataset like this, such a finding might indeed be more likely if the true function has some downward curvature. But that's not statistics, it's voodoo. All the associated statistical parameters, p-value, likelihood ratio, etc., are absolutely meaningless nonsense.)

deleted 3y ago
[deleted]
omgJustTest 3y ago
I think that in this case it is definitely a case of analytic-cotorsion. There is _heated_ and mostly opinionated debate about the low-dose regime. Briefly some people believe low-doses of radiation have a threshold (that one could get a certain amount before having excess cancer risk), there is "no-threshold" meaning relative risk = 1 and dose = 0, and there are people who say that some radiation is good for us because it stimulates our repair mechanisms and kills weak cells. All of this heated debate brings us to the currently misunderstood findings, and is why the author's say over-and-over that the linear model is a good fit. People, specifically the regulators and the nuclear industry, make a _bunch_ of health decisions on excess risk based on Linear-No-Threshold (LNT) models because they believe it is conservative. The main issue here is that in order to get good statistics, you have to irradiate people, and the two best datasets prior to today are Hiroshima survivors and the small number of low-dose exposures. People are torturing this analysis to resolve the debate, which in my opinion only feeds it.