Re: Cohen's Fallacy

["Rad health" <healthrad@HOTMAIL.COM>]

Ruth,



Regarding your first question about the inverse association between smoking 

and radon.  This is the reference (that Jim Muckerheide said I made up) that 

reports on the large inverse relationship (8th paragraph below).  Dr. Cohen, 

I looked for your reply to this correspondence to post, but I did not find 

it.



COHEN'S PARADOX



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



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



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

association. Health Physics Society Newsletter June 1998.

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

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

1995.

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

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

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

Phys. 75:23-28; 1998.

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

76:439-440; 1999a.

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

7.	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.

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

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

9.	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.

10.	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.

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

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

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

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

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

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

14.	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.

15.	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.

16.	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.

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

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

18.	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.

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

139:761-764; 1994.

20.	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.

21.	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.



















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