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Re: Risks of low level radiation - New Scientist Article



Jim Muckerheide wrote



Lubin, Samet, Smith, and other LNT-committed never produced a "refutation of 

Cohen's data" (and didn't refute Cohen's analysis either :-)  They only did 

a song-and-dance on why Cohen's study COULD be in error. They couldn't 

identify an error; and Cohen did produce quantitative analyses that refute 

their disingenuous rationalizations. Since nobody cares about science, but 

only that the LNT be sustained by NCRP/ICRP et al., they just need rhetoric 

to con innumerate politicians and the uninformed bureaucracy.



Jim

--------------------------------

Mr. Muckerheide,



I strongly disagree with you. Could your conviction on the invalidity of the 

LNT cloud your thinking? http://cnts.wpi.edu/RSH/About/board_directors.html 

I see Dr. Cohen is also on your founding board.



I think they made the point that they would need parallel individual level 

data next to the aggregate data to compare to it to disprove it (see below). 

  Obviously, that is not feasible.  I think it should be up to Cohen to 

prove his case, not up to others to do the impossible (see papers below). 

There must be better ways to prove or disprove the LNT than using ecological 

data! I don't think most epidemiologist take the findings seriously because 

of the limitations of the ecologic study design. I believe they noted quite 

a few errors and presented a very credible case against Cohen's papers. Our 

ramblings will not advance the discussion on this topic nor are we ever 

likely to agree on this topic.



I emailed Bill Fields about this issue today and he indicated it was futile 

to try to change your opinion.  He suggested people are free to address 

their concerns regarding his letter to the Health Physics Journal. Perhaps 

the head of Radiation Safety and Health could respond point-by-point to the 

letters below?



http://www.lww.com/health_physics/0017-90789-99ltrs.html



Jim Nelson







COHEN'S PARADOX

Dear Editors:



WE APPRECIATE the opportunity to respond to Cohen's letter-to-the-editor 

(Cohen 1999a) regarding our rejoinder (Field et al. 1998a). The rejoinder 

focused on Cohen's attempts (Cohen 1995,1997) to test the Linear 

No-Threshold Theory (LNTT) using ecologic data. In our initial publication 

on this topic (Smith et al. 1998), we demonstrated that Cohen erroneously 

used the wrong model to test the LNTT. We also demonstrated that when more 

valid Iowa county lung cancer rates were regressed on Cohen's mean county 

radon levels, the large negative associations Cohen noted between radon 

concentrations, obtained from short-term radon measurements, and lung cancer 

disappeared for Iowa. This letter addresses several important points that 

Cohen either continues to ignore or continues to contest.



Cohen (1999a) continues to challenge scientists to suggest a plausible 

explanation to explain the inverse relationship he notes between mean county 

residential radon measurements and mean county lung cancer mortality rates. 

We will call this inverse relationship "Cohen's Paradox." Cohen (1999a) 

states that his challenge is for someone to suggest a "not implausible 

model" as a possible explanation and that the burden of proof will be on him 

to show that "the explanation is highly implausible." We maintain that even 

if additional plausible models are offered, Cohen will likely not be able to 

explain his own paradox. Cohen has not accepted the fact that it may be 

impossible to explain Cohen's Paradox in definitive analytical terms with 

his existing data because it is not always possible to identify empirical 

sources of ecologic bias from aggregate (ecologic) data alone (Field et al. 

1998a).



Cohen (1999a) states that he has not been able to explain the inverse 

relationship (Cohen's Paradox) for his studies even with years of effort. We 

are not surprised. Cohen (1999a) continues to miss the point made previously 

(Greenland and Robins 1994; Smith et al. 1998; Field et al. 1998a) that 

characterizing biases is often extremely difficult in ecologic studies of 

geographic regions because of the high probability of interacting covariates 

that may differ across these regions. Greenland and Morgenstern (1989) point 

out that ecological control of a covariate contributing to ecologic bias 

will usually be inadequate to remove the bias produced by the covariate even 

in the absence of measurement error. Researchers (Greenland and Robins 1994; 

Lubin 1998; Smith et al. 1998; Archer 1998; Goldsmith 1999) have already 

presented very plausible theoretical examples of how Cohen's data can 

produce incorrect and even contradictory risk estimates. Cohen has rejected 

all of these examples.



Lagarde and Pershagen (1999) recently performed concurrent analyses on 

individual and aggregated data from a nationwide case-control study of 

residential radon and lung cancer in Sweden. The authors reported that the 

results confirm that ecologic studies may be misleading in studies of weak 

associations. So, are Cohen's negative point estimates a true effect or are 

they attributable to bias? To move the explanation beyond the theoretical 

level, analyses would require individual level data beyond the quality of 

Cohen's aggregate data.



Cohen continues to maintain that his ecologic studies avoided the ecologic 

fallacy, because he was testing the BEIR-IV LNTT model (Cohen 

1997,1998,1999a). Cohen also continues to deny our assertion (Smith et al. 

1998; Field et al. 1998a) that he was not testing the BEIR-IV LNTT model. As 

we stated (Smith et al 1998; Field et al. 1998a), Cohen's risk model is not 

the BEIR-IV risk model. Cohen attempted to equate his derived LNTT model to 

the BEIR-IV model by applying unsupported primary and secondary rigid 

assumptions. The assumptions all have both an error associated with them and 

a non-linear component, which as previously pointed out, cannot be 

quantitatively described. Rather than providing references to support the 

validity of his assumptions, Cohen defends his use of these assumptions 

(Cohen 1999a) by stating that they are the same assumptions as used in 

essentially all case-control studies.



All epidemiologic study designs have their own set of limitations. In fact, 

we have pointed out that inadequate measurement data can affect the validity 

of case-control studies as well as ecologic studies (Field et al. 

1996,1997). The limitations and assumptions of radon case-control 

epidemiologic studies, which use individual rather than ecologic data, have 

been presented elsewhere (Lubin et al. 1990; Field et al 1996). However, the 

nature of potential biases inherent in case-control studies is often quite 

different from an ecologic study. For example, case-control studies are not 

subject to cross-level bias. While case-control studies have their own 

inherent limitations, controlling for potential confounders in a 

well-designed case-control study is much easier than dealing with 

confounders in an ecologic study.



Many of the assumptions used by Cohen in his ecologic study design are not 

required for the case-control study design. For example, Cohen's ecologic 

studies assumed that smoking duration and intensity are the same for each 

individual within a specified region. Unlike ecologic studies, case-control 

studies collect data at the individual level so that detailed smoking 

histories can be available to use for adjustments.



We previously showed (Smith et al. 1998) that when Cohen's adjusted smoking 

percentages for males and females were regressed on radon levels, 

significant (p < 0.00001) negative associations between smoking and radon 

were noted for both males and females. In addition, when we (Smith et al. 

1998) repeated the regression of lung cancer mortality rates on Cohen's 

adjusted smoking percentages, the resulting R2 values indicated that Cohen's 

smoking summary data explained very little (23.7% for females; 34.5% for 

males) of the variation in lung cancer mortality rates. It is not surprising 

Cohen cannot control for these risk factors using aggregate data. In 

addition, Cohen's ecologic studies make numerous other assumptions not 

required for newer case-control designs (Field et al. 1996).



Cohen (1999a) continues to offer explanations for how the conclusions of 

other published ecologic studies can be wrong. We (Field et al. 1998a) 

offered the large scale ecologic study by Menotti et al. (1997) as an 

example of an inverse relationship between average blood pressure and stroke 

mortality rates. Cohen did not have actual data from the study (Menotti et 

al. 1997) and therefore could not attempt to explain the paradoxical finding 

in definitive analytical terms. However, we would be interested in Cohen's 

definitive analytical explanation for why the large negative associations 

disappeared for the Iowa data when we regressed the more valid county lung 

cancer rates for Iowa on Cohen's own mean county radon levels for Iowa 

(Smith et al. 1998).



As we mentioned previously, Iowa serves as an ideal site for a radon 

epidemiologic study because it possesses the highest mean radon 

concentrations in the United States (White et al. 1992), a population with 

low mobility, and a quality cancer registry (Field et al. 1996). In 

addition, because of the large number of counties in Iowa (99), Iowa data 

provide a finer ecologic breakdown per percent population compared to much 

of the rest of the U.S. data set assembled by Cohen. Cohen offers to provide 

specific quantitative explanations for why other ecologic studies can give 

false results, but he has yet to provide a persuasive argument for our 

findings in Iowa (Smith et al. 1998), which predominantly use his aggregate 

data.



Cohen (1999b) states, "I have never claimed that our studies support 

hormesis, since such an interpretation suffers from the ecologic fallacy." 

We agree with that claim by Cohen. However, Cohen has not provided 

persuasive evidence to show that his test of the LNTT also does not suffer 

from the ecologic fallacy. Cohen attempts to test the LNTT by analyzing 

averaged multivariate distributions of aggregate data, followed by analyses 

using more county level summaries to correct the potential biases. Because 

of the heterogeneity within the county summaries, the aggregate data provide 

very little confounder control, especially in the presence of non-linear 

dependencies (Field et al. 1998b) and interactions (Greenland and Robins 

1994). It is a fallacy to think that Cohen's inferences made from aggregate 

level data can be applied to individual level exposure-response 

relationships, especially when Cohen is not even testing the BEIR-IV 

formula.



Piantadosi (1994) pointed out that "a single result at odds with theory 

should not discredit the theory unless the source of data and analysis meet 

the most rigorous methodological criteria." Cohen's data, assumptions, and 

study design failed to fulfill these criteria. Nobel Laureate, Sir Peter 

Medawar (1979) wrote, "I cannot give any scientist of any age better advice 

than this: the intensity of the conviction that a hypothesis is true has no 

bearing on whether it is true or not. The importance of the strength of our 

conviction is only to provide a proportionately strong incentive to find out 

if the hypothesis will stand up to critical evaluation." Time will tell if 

the LNTT will stand up to critical evaluation or fall. Nevertheless, we 

oppose the critical evaluation taking the form of an ecologic study.





R. William Field

Brian J. Smith

Charles F. Lynch



College of Public Health

Department of Epidemiology

N222 Oakdale Hall

University of Iowa

Iowa City, IA 52242

References



Archer, V. E. Cohen's home radon-lung cancer data suggests positive 

association. Health Physics Society Newsletter June 1998.

Cohen, B. L. Test of the linear no-threshold theory of radiation 

carcinogenesis for inhaled radon decay products. Health Phys. 68:157-174; 

1995.

Cohen, B. L. Lung cancer rate vs. mean radon levels in U.S. counties of 

various characteristics. Health Phys. 72:114-119; 1997.

Cohen, B. L. Response to criticisms of Smith, Field, and Lynch. Health Phys. 

75:23-28; 1998.

Cohen, B. L. Response to "Rejoinder" by Field et al. Health Phys. 

76:439-440; 1999a.

Cohen, B. L. Comment on letter by Straja and Moghissi. 76:318; 1999b.

Field, R. W.; Steck, D. J.; Lynch, C. F.; Brus, C. P.; Neuberger, J. S.; 

Kross, B. C. Residential radon-222 exposure and lung cancer: exposure 

assessment methodology. J. Exposure Analysis and Environmental Epidemiology 

6:181-195; 1996.

Field, R. W.; Steck, D. J.; Neuberger, J. S. Accounting for random error in 

radon exposure assessment. Health Phys. 73:272-273; 1997.

Field, R. W.; Smith, B. J.; Lynch, C. F. Ecologic bias revisited, a 

rejoinder to Cohen's response to "Residential 222Rn exposure and lung 

cancer: testing the linear no-threshold theory with ecologic data". Health 

Phys. 75:31-33; 1998a.

Field, R. W.; Smith, B. J.; Brus, C. P.; Lynch, C. F.; Neuberger, J. S.; 

Steck, D. J. Retrospective temporal and spatial mobility of adult Iowa 

women. Risk Analysis: An International Journal 18:575-584; 1998b.

Goldsmith, J. R. The residential radon-lung cancer association in U.S. 

counties: a commentary. Health Phys. 76:553-557; 1999.

Greenland, S.; Morgenstern, H. Ecological bias, confounding, and effect 

modification. International J. Epidemiol. 18:269-274; 1989.

Greenland, S.; Robins, J. Invited commentary: ecologic studies-biases, 

misconceptions, and counterexamples. Am. J. Epidemiol. 139:747-760; 1994.

Lagarde, F.; Pershagen, G. Parallel analyses of individual and ecologic data 

on residential radon, cofactors, and lung cancer in Sweden. Am. J. 

Epidemiol. 149:28-274; 1999.

Lubin, J. H.; Samet, J. M.; Weinberg, C. Design issues in epidemiologic 

studies of indoor exposure to radon and risk of lung cancer. Health Phys. 

59:807-817; 1990.

Lubin, J. H. On the discrepancy between epidemiologic studies in individuals 

of lung cancer and residential radon and Cohen's ecologic regression. Health 

Phys. 75:4-10; 1998.

Medawar, P. B. Advice to a young scientist. Reading, MA: Basic Books, A 

Subsidiary of Perseus Books, L.L.C.; 1979.

Menotti, A.; Blackburn, H.; Kromhout, D.; Nissinen, A.; Karvonen, M.; 

Aravanis, C.; Anastasios, D.; Fidanza, F.; Giampaoli, S. The inverse 

relation of average blood pressure and stroke mortality rates in the seven 

countries study: A paradox. European J. Epidemiol. 13:379-386; 1997.

Piantadosi, S. Invited commentary; Ecologic biases. Am. J. Epidemiol. 

139:761-764; 1994.

Smith, B. J.; Field, R. W.; Lynch, C. F. Residential 222Rn exposure and lung 

cancer: testing the linear no-threshold theory with ecologic data. Health 

Phys. 75:11-17; 1998.

White, S. B.; Bergsten, J. W.; Alexander, B. V.; Rodman, N. F.; Phillips, J. 

L. Indoor 222Rn concentrations in a probability sample of 43,000 houses 

across 30 states. Health Phys. 62:41-50; 1992.



--------------------------------------------------------------------------------



Response to Cohen's comments on the Lubin Rejoinder

Dear Editors:



IN HIS recent rebuttal to my paper and its rejoinder (Lubin 1998a; Lubin 

1998b), Cohen (Cohen 1999) made several errors of fact and inference, which 

results in misleading conclusions. Cohen concluded that his ecologic 

regression provided a good fit to the results of indoor radon studies. 

However, this conclusion was based on an invalid probabilistic argument. 

Further, based on both visual and formal statistical evaluations, his model 

fails to fit indoor radon data, and as such his conclusions are incorrect.



Cohen made factual errors when explaining relative risks (RR) which were 

markedly discrepant from model predictions. He stated that the Stockholm 

radon study lacked information on smoking, only 10% of the houses had radon 

measurements, and he was "unable to relate" a data point. However, 

investigators had smoking data on nearly all subjects and measured 

sufficient numbers of houses to cover nearly 80% of the exposure period 

between 1945 and 5 y prior to the 1983-1985 enrollment period (Pershagen et 

al. 1992). Cohen's unknown RR has value 1.7 with 95% CI (1.0,2.9) (Table 2 

in Pershagen et al. 1992). Cohen also alluded to a "horizontal error bar" in 

the Missouri study. He implied that the mean did not represent the 

category-specific radon value. However, a simple inspection shows that his 

regression line lies entirely outside the 95% confidence interval (CI), 

regardless of the precise location of the RR.



Cohen suggested that his ecologic model fitted the indoor radon data because 

"only 2 of 33" of the 95% CIs failed to include his regression line. This is 

an incorrect probabilistic interpretation of CIs. A 95% CI provides that 

level of assurance that the true parameter lies somewhere within the 

interval. However, Cohen treated the CIs as if they were independent tests 

of the null hypothesis. RRs and CIs within each radon study are not 

independent, but statistically dependent. This is obvious since changing the 

definition of the baseline (i.e., lowest exposure) category alters all RRs 

and CIs. In theory one could encompass Cohen's regression line entirely 

within the CIs by simply defining narrower radon categories and thereby 

increasing widths of the CIs. Thus, Cohen's "assessment of model fit" is not 

valid.



Cohen suggested that his model fitted the RRs for individual studies. 

However, this also is incorrect as a variety of methods shows that his risk 

model based on descriptive data provides a poor characterization of lung 

cancer risks in the more analytically sound indoor radon studies. Fig. 1 

shows category-specific relative risks (RR) from nine case-control studies 

(eight studies from a meta-analysis (Lubin and Boice 1997), and the new 

Cornwall study (Darby et al. 1998), the BEIR VI miner-based extrapolation 

(National Research Council 1999), and Cohen's linear-quadratic ecologic 

model. All models are adjusted to pass through 22 Bq m-3, which is used to 

represent the mean of the lowest concentration category, although mean radon 

for the lowest category ranged from 22 to 55.5 Bq m-3. It is apparent that 

visually Cohen's model does not fit the indoor radon data. Cohen's model 

predicts a protective effect of radon in the range 22 to 291 Bq m-3, while 

the data show no evidence of a protective effect in this range.



Fig. 2 shows RRs for each indoor radon study, along with three regression 

lines. The models are as follows: (1) a log-linear model (dotted line): 

RR(x) = exp[[beta](x-xo], where x is radon concentration, xo is the mean 

concentration for the lowest category, and [beta], the unknown parameter, is 

estimated within study by weighted least squares regression; (2) Cohen's 

linear-quadratic ecologic regression model (dashed line); (3) the linear 

excess RR model (solid line): RR(x) = 1 + [beta]x, where [beta] is fixed at 

0.001107 Bq m-3. This value is derived from the excess RR estimate of 

0.0117/Working Level Month and assuming 25 y exposure (National Research 

Council 1999). This model was used for simplicity, since it closely 

approximates extrapolations of the BEIR VI model. The pooled model from the 

meta-analysis was similar to the BEIR VI extrapolation and was omitted. All 

models were adjusted to pass through the mean radon level of the lowest 

category in each study. In contrast to Cohen's presentation (Cohen 1999) 

plots use a common scale to avoid perceptual distortion. Because Cohen's 

model predicts RRs less than 1 under about 300 Bq m-3, Cohen's model 

predictions are less disparate for "negative" studies, such as the Winnipeg 

and Shenyang studies.



A variety of methods can be used to formally compare model fits, and all 

show a poor fit for Cohen's ecologic model. First, very simply one can count 

the number of RRs falling below and above the various prediction lines. 

Within each study, the study-specific log-linear models generally provided 

good fits to the RRs, and not surprisingly 15 of 28 RRs fell below the 

prediction lines. Based on a binomial distribution, the p-value of observing 

this number or a number more extreme is p = 0.43. For the miner model, 15 of 

29 points fell below the prediction lines with p = 0.50. For Cohen's model, 

7 of 31 fell below the prediction lines with p = 0.002.







--------------------------------------------------------------------------------









--------------------------------------------------------------------------------

Fig. 1. RRs from nine lung cancer case-control studies of indoor radon. 

Dotted line depicts extrapolation of RR from miners (National Research 

Council 1999); dashed line depicts linear-quadratic ecologic regression 

model (Cohen 1999); and solid line depicts a RR of one.

--------------------------------------------------------------------------------



Using residual sums of squares, F-statistics were calculated comparing the 

fit of the indoor polling model, the BEIR VI model, and the Cohen 

linear-quadratic model to the study-specific log-linear model. The values of 

the F-statistics are valid comparisons of model fit. However, p-values for 

the BEIR VI model and Cohen model are only approximate because those models 

are not nested in the (log-linear) study-specific model, which is only 

approximately linear. Fig. 3 shows the F-statistics and 95% and 99% 

quantiles. In 7 of 9 studies, the F-statistic for the Cohen model exceeds 

the values for the other models.







--------------------------------------------------------------------------------









--------------------------------------------------------------------------------

Fig. 2. RRs from nine lung cancer case-control studies of indoor radon. 

Solid line shows fitted log-linear model to data from each study; dotted 

line depicts extrapolation of RRs from miners (National Research Council 

1999); dashed line depicts linear-quadratic ecologic regression model (Cohen 

1999).

--------------------------------------------------------------------------------



The Pearson chi-square goodness-of-fit statistics (sum over a study of the 

squared difference of the observed and expected RR divided by the expected) 

showed the same results.







--------------------------------------------------------------------------------









--------------------------------------------------------------------------------

Fig. 3. F-statistics for comparison of each model relative to the 

study-specific one-parameter, log-linear model. Solid and dashed lines 

depict 0.05 and 0.01 quantiles of the F-distribution, respectively.

--------------------------------------------------------------------------------





I agree with Cohen's statement that average dose does not determine average 

risk. However, the logical consequence of this fact is that any functional 

relationship between average dose and average risk provides no direct 

information about the relationship between individual dose and individual 

risk. Proponents of ecologic studies seem not to accept the fact, 

demonstrated both theoretically (Lubin 1998a) and practically (Lagarde and 

Pershagen 1999), that bias in ecologic studies can occur due to 

within-county correlations among risk factors and that the correlations, 

which may vary across counties, cannot be modeled using only county level 

data. Even small correlations among risk factors can induce large biases at 

the county level, which cannot be "adjusted" using area level data, 

regardless of how finely counties are stratified. Thus, no valid inference 

can be made from a county-level relationship to individual 

exposure-response, and conversely, absent information on within-county 

correlations, no valid inference can be made from the exposure-response for 

individuals to the county-level. As a result, there is no unambiguous way to 

test a LNT model for individual radon exposure using only county data. Cohen 

has attempted to validate his model by suggesting consistency with indoor 

radon studies. My analyses show clearly that the Cohen model just does not 

agree with results from the indoor radon studies.





Jay H. Lubin



Division of Cancer Epidemiology and Genetics

National Cancer Institute

6120 Executive Blvd., EPN/8042

Rockville, MD 20892-7244

References



Cohen, B. L. Response to the Lubin rejoinder. Health Phys. 76:437-439; 1999.

Darby, S.; Whitley, E.; Silcocks, P.; Thakrar, B.; Green, M.; Lomas, P.; 

Miles, J.; Reeves, G.; Fearn, T.; Doll, R. Risk of lung cancer associated 

with residential radon exposure in Southwest England: a case-control study. 

Br. J. Cancer 78:394-408; 1998.

Lagarde, F.; Pershagen, G. Parallel analyses of individual and ecologic data 

on residential radon, cofactors, and lung cancer in Sweden. Am. J. 

Epidemiol. 149:268-274; 1999.

Lubin, J. H. On the discrepancy between epidemiologic studies in individuals 

of lung cancer and residential radon and Cohen's ecologic regression. Health 

Phys. 75:4-10; 1998a.

Lubin, J. H. Rejoinder: Cohen's response to "On the discrepancy between 

epidemiologic studies in individuals of lung cancer and residential radon 

and Cohen's ecologic regression." Health Phys. 85:29-30; 1998b.

Lubin, J. H.; Boice, J. D. J. Lung cancer risk from residential radon: 

meta-analysis of eight epidemiologic studies. J. Natl. Cancer Inst. 

89:49-57; 1997.

National Research Council. Health effects of exposure to radon: BEIR VI. 

Washington, DC: National Academy Press; 1999.

Pershagen, G.; Liang, Z. H.; Hrubec, A.; Svensson, C.; Boice, J. D. J. 

Residential radon exposure and lung cancer in Swedish women. Health Phys. 

63:179-186; 1992.











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