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