RADIATION RESEARCH 183, 476–484 (2015)0033-7587/15 $15.00
Ó2015 by Radiation Research Society.
All rights of reproduction in any form reserved.
DOI: 10.1667/RR4029.1

LETTERS TO THE EDITOR
induction of chromosomal aberrations arising from double-strand breaks in
DNA. Aberration induction was judged to be linear quadratic based onmost critical studies that had sufficient statistical power (8). Also, thenavailable animal data supported the linear-quadratic model (e.g. 9, 10).
The BEIR VII committee understood that the linear-quadratic model could
be approximated by a linear function in the low-dose (,100 mSv) region(see Fig. 1).
The BEIR VII committee had a clear-eyed view of the challenges for
estimating radiation risks at low doses:

Comments on Estimating Risks of Low Radiation Doses—A
Critical Review of the BEIR VII Report and Its Use of the
Linear No-Threshold (LNT) Hypothesis by Edward J.
Calabrese and Michael K. O’Connor
Kevin D. Crowley,a,1 Harry M. Cullings,b Reid D. Landes,b Roy E.
Shoreb and Robert L. Ullrichb
National Research Council, National Academy of Sciences, Washington,
DC 20001; and b Radiation Effects Research Foundation, 5-2 Hijiyama
Park, Minami-ku, Hiroshima City, Japana‘‘It is abundantly clear that direct epidemiologic and animal approaches to
low-dose cancer risk are intrinsically limited in their capacity to define
possible curvilinearity or dose thresholds for risk in the range 0–100 mSv.
For this reason, the present [BEIR VII] report has placed much emphasison the mechanistic data that can underpin such judgments’’ (p. 245).

Calabrese and O’Connor (1) (hereafter denoted as C&O) offer
commentary on the origin of the linear no-threshold (LNT) model for
low-dose radiation risk in the context of the National Academy of
Sciences’ (NAS’) Biological Effects of Ionizing Radiation VII (BEIR VII)
report (2). We write to correct several mischaracterizations andinaccuracies in their commentary. Our comments are organized according
to the sections in C&O’s article.

Future BEIR committees may reach different conclusions about the validityof the LNT model based on advances in science and medicine. Those futurecommittees will not be bound by previous BEIR reports, and certainly not by
seven-decade-old science, in reaching their conclusions.

LNT MODEL AND BEIR: HISTORICAL
FOUNDATIONSTHE CASE FOR AND AGAINST THE LNT MODEL

Calabrese and O’Connor assert that the use of the LNT model by NAS
BEIR committees is the direct result of efforts by Hermann Muller and his
radiation genetics colleagues to persuade national and international
committees to drop their historical reliance on threshold dose-responsemodels (p. 464). According to C&O, Muller finally achieved success byconvincing the Biological Effects of Atomic Radiation (BEAR) committee(3) to adopt the LNT model. This characterization of Muller’s actions has
been disputed by Cicerone and Crowley (4); the first author is president of
the National Academy of Sciences.
Scientific advances over the past seven decades have prompted areexamination of the risks arising from exposure to low-dose, low-LET
radiation by several BEIR committees (2, 5–7). The most recent BEIR
report (2) includes a detailed review of the then-current scientificunderstanding of biological responses to ionizing radiation. Based on thisanalysis, the committee that authored the BEIR VII report (2) concludedthat

Calabrese and O’Connor note (p. 465) that ‘‘decisions made by thevarious BEIR Committees (with respect to models used to generate risk
estimates) are often at odds with those from prior BEIR Committees andmay well change with the next iteration of the BEIR process’’. The authors
also assert that the published scientific literature does not support themagnitude of changes in the risk estimates across these BEIR studies.
It is certainly true that risk models and risk estimates have changed
across the BEIR studies as scientific understanding and analyticalcapabilities have improved. Low-dose cancer risk assessment is far fromsettled science: It is technically challenging to estimate cancer risks arisingfrom low-dose ionizing radiation based on observational studies alone
because of confounding influences that can vary among and within study
cohorts. More generally, it is important to carefully consider the results of
all available studies when developing risk estimates, paying due attention
to statistical power and the potential for uncontrolled confounding.

‘‘. . . the balance of scientific evidence at low doses tends to weigh infavor of a simple proportionate relationship between radiation dose andcancer risk’’ (p. 246).

SOURCES OF DATA OF (sic) STOCHASTIC EFFECTS
OF IONIZING RADIATION
Calabrese and O’Connor discussed and critiqued the use of four classesof data in the BEIR VII report. Some of their critiques suffer from theselective use of study results and/or the failure to use the full range ofavailable scientific information. Examples follow.

The committee’s reasoning to support this conclusion is laid out clearlyon p. 245–246 of the BEIR VII report (2). C&O may disagree with thisconclusion, but they should acknowledge that the BEIR committee
undertook an independent analysis based on contemporary data rather than
implying that the committee relied on 1950s era science.
The BEIR VII committee analyzed then-available radiobiology andexperimental data to identify plausible cancer risk models. The committee
judged that the primary mechanism driving cancer development was

Environmental Studies
The studies cited in this section of Calabrese and O’Connor’scommentary do not support any particular conclusions about the validityof the LNT model because of study design or statistical power limitations.
In particular, ecologic studies have well-known limitations arising fromthe lack of individual dose estimates and information on possible
confounding factors.

1 Address for correspondence: National Research Council, Nuclear
and Radiation Studies Board, 500 Fifth Street, NW, Washington, DC
20001; e-mail: kcrowley@nas.edu.

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477

FIG. 1. Figure 10-1 from the BEIR VII report (2) showing a hypothetical dose-response curve. The solid
curve is a representation of the true dose response for excess relative risk. This curve can be approximated with alinear function at low doses (shown by the line tangent to the curve at zero dose). DDREF is the ratio of the slopeof the zero-dose tangent line and the slope of a linear approximation of the curve at higher dose. The low-dose
region, taken to be ,100 mGy absorbed dose (equivalent to ,100 mSv effective dose), is demarcated by thevertical dashed lines on the left side of the plot. Figure reprinted with permission of the National Academy of
Sciences, Courtesy of the National Academies Press, Washington, DC.
Calabrese and O’Connor cite a conclusion from a study performed byThompson et al. (11) on residential radon exposure in Worchester County,
Massachusetts, as ‘‘running contrary’’ to a statement in an NAS media
release on the BEIR VII report that suggested ways to reduce radon
exposures (and potential cancer risks arising from such exposures). This isa good example of C&O’s selective reporting of research results to makebroad and scientifically unsupported insinuations.
The Thompson et al. (11) study, which involved 200 cases and 397
controls, concluded that the ‘‘possibility of a hormetic effect on lung cancer
at low radiation doses cannot be excluded’’ (C&O, p. 466; see also their Fig.
12). Failing to exclude an effect does not constitute evidence for its existence.
Furthermore, C&O ignore much larger and more statistically powerful
European (12), North American (13) and Chinese (14) residential radonstudies that do not show evidence for hormesis. These studies collectively
included over 12,000 lung cancer cases and 21,000 controls. All dose
categories referenced in these studies had estimated adjusted odds ratios orrelative risks above 1.0; moreover, the risk estimates tended to increase withdose [e.g., see Table 2 in ref. (12); Table 9 in ref (13) and Table 3 in ref (14)].
Calabrese and O’Connor also ignore the results from the long-term
follow-up of the Russian Techa River cohort, which received low-dose,
low-dose-rate exposures from environmental contamination. This cohortshows evidence of a radiation dose-response, similar to that seen in theatomic bomb survivors, for end points including solid cancer mortality(15) and leukemia (16).

‘‘As the BEIR VII report indicated, in most of the nuclear industry
worker studies, rates for all causes and all cancer mortality in the workers
were substantially lower than the reference population. The BEIR VII
Committee did not attempt to ascertain why, but speculated that it may be
due to a ‘healthy worker effect and unknown differences between nuclear
industry workers and the general public’. Consequently the BEIR VII
Committee concluded that occupational studies were not suitable for the
projection of population-based risks and eliminated them from further
consideration in its risk estimates.’’
The BEIR VII committee’s conclusions were not based on ‘‘speculation,’’ but rather on an extensive analysis of then-available nuclear worker
studies. The committee identified potentially useful studies (Table 8-1),
summarized their characteristics (Table 8-2) and risk estimates (Table 8.3–8.4), and discussed the challenges for using the studies for populationbased risk estimation:‘‘Because of the uncertainty in occupational risk estimates and the fact thaterrors in doses have not formally been taken into account in these studies,
the [BEIR VII] committee has concluded that the occupational studies are
currently not suitable for the projection of population-based risks. Thesestudies, however, provide a comparison to the risk estimates derived fromatomic bomb survivors’’ (p. 206).

Medical Radiation Studies
Occupational Radiation Studies
Calabrese and O’Connor mischaracterize the BEIR VII committee’s
conclusions about the usefulness of nuclear worker studies for risk
estimation, which appear in Chapter 8 of BEIR VII (2). C&O comment (p.
466) that:Calabrese and O’Connor’s Fig. 2 reproduces results from the Lundell etal. (17) study of breast cancer in subjects receiving radiotherapy for skinhemangioma. C&O conclude, based on a visual inspection of data in thisfigure, that there are no increases in breast cancer risk at doses below 100
mSv. C&O do not cite Lundell and colleagues’ own conclusion that a

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LETTERS TO THE EDITOR

FIG. 2. Linear (top) and semi-logarithmic (bottom) plots of data from Table 4 of Preston et al. (19). The lines
in these plots are fitted without any weighting or other adjustment and do not represent results obtained by
Preston et al. These lines are shown only to illustrate the visual appearance differences described in the text.
Error bars indicating uncertainties in individual dose categories are omitted to simplify visual comparisons.
linear model provided the best fit based on a more rigorous statisticalanalysis of the data.
Calabrese and O’Connor also did not cite the more recent study of thesame cohort by Eidemüller et al. (18), which investigated risk-thresholdmodels in two contexts: excess relative risk (ERR) and two-stage clonal
expansion. In both contexts, the threshold models had a deviance (ameasure of model fit) that was worse than the no-threshold model (Table 1
in Eidemüller et al.). Eidemüller et al. commented that:‘‘Indeed, it is seen that for a threshold value of 100 mGy the deviance iseven larger. Different threshold values between 0 and 500 mGy give
similar results. Thus we neither find support for a threshold model nor canit be ruled out’’ (p. 51).

Atomic Bomb Survivor Studies
Calabrese and O’Connor mischaracterize the source of risk estimatesfrom the life span study (LSS) cohort that are presented in the BEIR VII

report. They comment (p. 467) that the BEIR VII committee relied on risk
estimates ‘‘produced by researchers from the RERF,’’ implying that thecommittee was unable to design or carry out its own analyses. Although
RERF researchers provided assistance to the BEIR VII committee indeveloping risk estimates, the committee was responsible for determining
how the risk assessments would be conducted and it performed the analyses.
Calabrese and O’Connor present data from the LSS cohort to show that‘‘at doses up to ;100 mGy, no increase in the number of cancers isobserved’’ (p. 467–468). This conclusion is based on the visual inspectionof a semi-logarithmic plot (C&O’s Fig. 4) that contains only two data
points between 0–100 mGy. These data points represent the midpoints of
two broad dose categories (0–5 and 5–100 mGy) taken from Table 4 of
Preston et al. (19).
Calabrese and O’Connor’s use of a semi-logarithmic plot dramaticallystretches out the low-dose portion of the dose range and prevents readersfrom visually evaluating the linearity of the data. Indeed, even a simple
linear regression of the points in C&O’s plot would look flat at low doses
because of the logarithmic scale. Figure 2 illustrates the differences in

LETTERS TO THE EDITOR

appearance between linear and semi-logarithmic plots of the data from
Table 4 of Preston et al. (19). The slope of the fitted curve in the semilogarithmic plot in Fig. 2 looks nearly flat below 0.1 Gy as observed by
C&O. However, the slope of the fitted curve in the linear plot in Fig. 2 isclearly greater than zero. Of course, the appearance of these plots also
depends on the placement of boundaries for the dose categories.
Nevertheless, the results in individual dose categories are actually quite
close to the linear regression fitted on the dose interval from 0–2 Gy, i.e.,omitting the highest dose category due to the leveling-off at high doses(Fig. 1).
There is a more fundamental problem with C&O’s visual approach,which is used throughout their paper, for evaluating dose-response
relationships: visual examination is a necessary and important first step forassessing data patterns, but it is insufficient for developing quantitatively
supportable conclusions about data relationships. The conclusions ofOzasa et al. (20) about low-dose cancer risks are based on rigorous fitting
and comparison of models that represent different hypotheses about theradiation induction of cancer risk [e.g., linear, linear quadratic, threshold(Fig. 1)]. This is the same approach that was used by the BEIR VII
committee.
Calabrese and O’Connor suggest (p. 468) that the dose-thresholdanalysis for solid cancer mortality that was performed by Ozasa et al. (20)is inconsistent with an LNT model because the slope for the dose-response
fit was higher below 0.1 Gy than above 0.1 Gy. C&O neglect to note thatthese slope differences are not statistically significant and have wide
confidence intervals.
Ozasa et al. compared the LNT model, which has a zero threshold, to alinear-threshold (LT) model, which has an additional parameter for anonzero threshold. They concluded that for no value of the thresholdparameter did the LT model fit significantly better than the LNT model.
Their analysis gave a best estimate of the threshold dose as 0 Gy (i.e. nothreshold) and a 95% confidence interval of (0, 0.15) Gy. An analysis byPreston et al. (19) was also compatible with a 0 Gy dose threshold.
Calabrese and O’Connor state (p. 468) that ‘‘an analysis by Doss . . .
using a more flexible model showed that the LSS data does not support azero dose threshold and concluded that there was too much variability inthe data to draw any conclusion as to the existence or absence of athreshold’’. The first part of this statement is inconsistent with the second
part in the usual context of hypothesis testing, which is the basis for the
results obtained by Ozasa et al. In statistically testing whether or not there
is a threshold, the usual default assumption (‘‘null hypothesis’’) is that
there is no threshold, i.e., that the threshold is zero, as this is the simpler
and more parsimonious model. However, when a term for a threshold is
added to the model, if the best-fitting value for the threshold is .0 andsignificantly improves the fit to the data (i.e., if the confidence interval forthe location of the threshold does not include zero) the default assumptionof no threshold is rejected. If the default assumption is a zero-dose
threshold, to say that the data ‘‘does not support a zero dose threshold’’equates to saying that the data support a statistically significant parameter
estimate for a nonzero threshold, which is inconsistent with the statementthat ‘‘there was too much variability in the data to draw any conclusion as
to the existence or absence of a threshold’’.
The origin of the inconsistency is better understood by examining theanalysis on which Doss based the cited statement. The criterion used by
Doss (21) (p. 497 and Fig. 2) was based on visual inspection of theconfidence intervals for risk associated with dose categories, as to whether
they include zero risk, or in Doss’s words, whether ‘‘lower bounds of thepoint-wise 95% CIs would have been below zero for low doses’’. This is
not a statistical test of the shape of the dose-response function, as may beappreciated from the simple observation that the widths on the risk axis of
the confidence intervals depend on the widths of the dose categories on thedose axis and become arbitrarily wide as the dose categories are madenarrower. And even if properly estimated confidence bands for the dose
response had lower bounds below zero at low doses, this would not

479

exclude a threshold at zero—it would be upper confidence bounds at or
below zero on a dose interval from zero to some dose T . 0 that would
exclude a threshold below T, because they would exclude risk .0 at dose
,T.
More importantly, the article by Doss that is cited by C&O purports togive evidence not only of a threshold but also a hormetic effect. This claim
is based on the assertion that the observed baseline cancer rates in the LSS
are somehow 20% too low, based on a study by Hwang et al. (22) of acohort in Taiwan that was exposed to protracted gamma radiation fromcontaminated concrete rebar in a residential building. The Hwang et al.
study has far less statistical power than the LSS (7,271 vs. 93,746 subjects)
and far less follow-up, among other concerns, starting with the fact that it
represents an entirely different population with follow-up beginning in adifferent calendar period than the LSS. Doss’s use of the Hwang et al.
study to artificially adjust the baseline rates of the LSS in a risk regressionis unsupportable.

Risk Models
Calabrese and O’Connor critique the models selected by the BEIR VII
committee to generate low-dose cancer risk estimates. They note that thelung cancer studies cited in the BEIR VII report display a wide variability
in estimated ERR versus average lung dose (see C&O’s Fig. 5). C&O state(p. 469) that ‘‘Ideally all [ERR] estimates should be identical and should
all lie within one or two standard deviations of each other’’. This is notstrictly correct: ERR uncertainty estimates for individual studies do notprovide a statistical basis for drawing conclusions about the expectedvariability across studies without considerable additional assumptions andmodeling. Indeed, the BEIR VII report (2) notes that ‘‘Although risk
estimates from these studies vary, confidence intervals are very large andthe estimates shown are therefore statistically compatible’’ (p. 175).
The uncertainty estimates in the individual studies cited in the BEIR VII
report are typically based on sampling error (i.e. uncertainty due to thefinite number of subjects in a study) and do not include additional
uncertainty due to factors that may vary among studies, including
uncontrolled confounders and attributes of the individual cohorts, among
many others. Indeed, the BEIR VII report (2) notes that ‘‘Since theconditions of exposure, the characteristics of the study populations, andthe extent and quality of the dosimetry and follow-up differ widely, therisk estimates derived for individual studies are not strictly comparable’’(p. 174).
Calabrese and O’Connor note that the mean ERR estimate for lungcancer studies of medically exposed populations cited in the BEIR VII
report (0.05/Gy) is 17 times smaller than the ERR estimate based on
atomic bomb survivor data (0.86/Gy). C&O assert (p. 469) that thedifferences among the individual studies cited in the BEIR VII reportillustrate ‘‘the tremendous uncertainties in estimating the risk factor for asingle organ and the dangers in making any risk estimate based on thisdata’’. However, the ERR estimates from medically exposed populations
and atomic bomb survivors are not strictly comparable; the medically
exposed populations may differ from atomic bomb survivors with respect
to a variety of factors, for example, the mean age at exposure, fractionatedvs. single exposures, and the presence of pre-existing medical risk factors
in the medically exposed populations.
Calabrese and O’Connor critique the BEIR VII committee’s use of ERR
and excess absolute risk (EAR) models to estimate the Lifetime
Attributable Risk (LAR) for various radiogenic cancer sites in U.S.
populations. C&O observe that the BEIR VII LAR estimates based on the
ERR and EAR models differed appreciably for some cancer sites; theyassert (p. 469) that ‘‘Given that both models are essentially based on the
RERF studies, one would expect reasonable agreement between themodels for most cancers’’ (p. 469). In fact, there is no reason to expect
agreement between ERR and EAR models from Japanese to Western
populations when the background rates for a cancer site differ appreciably

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LETTERS TO THE EDITOR

between the two populations. Indeed, if C&O’s statement were correct the
BEIR VII committee would have been far less concerned about model
selection. Because information about radiogenic cancer risks for many
cancer sites in Western populations is limited, the BEIR VII committee
used a combination of the ERR and EAR models to develop LAR
estimates and commented that‘‘Although it is likely that the correct transport model [of risk] varies bycancer site, for sites other than breast, thyroid and lung, the Committee
judged that current knowledge was insufficient to allow the approach tovary by cancer site’’ (p. 276).
Calabrese and O’Connor assert (p. 469) that the use of a dose and doserate effectiveness factor (DDREF) in the BEIR VII risk model ‘‘converts
the LNT into a linear-quadratic or biphasic model, and provides a meansof modifying the linear model without officially abandoning the LNT
hypothesis’’. In fact, as noted previously in this commentary, the BEIR
VII committee recognized that the dose-response model is probably linear
quadratic over the broader dose range (see Fig. 1), with the low-dose
portion of the curve being essentially linear. Consequently, the use of the
DDREF does not invalidate the linear model at low doses as C&O
suggest.

REGULATORY ISSUES AND LNTCalabrese and O’Connor scold the legislative, regulatory and scientificcommunities for ignoring what the authors refer to as ‘‘reality checks’’ (p.
471) that have challenged the LNT model for chemical carcinogens. Theynever explain how this discussion relates, if at all, to regulations forionizing radiation.
More importantly, C&O’s mixing of science and policy advocacy intheir commentary is unhelpful for promoting the credibility of radiation
science in the policy and public domains. Scientists can do grave damage
to their credibility, and to the credibility of the broader scientificenterprise, when they use science to advocate for personal policy choices.
Science is an important input to policy decisions on chemical and radiation
regulations, but it is not the only input. Such decisions usually involve a
number of nonscientific factors, for example regulatory costs and benefits
as well as public preferences. The Administrative Procedure Act (Public
Law 79-404) requires U.S. government agencies to provide for public
participation in regulatory decision making.
The U.S. Environmental Protection Agency (EPA) has the primary
responsibility for setting radiation protection standards in the UnitedStates. The BEIR reports are an important input to those standards, but the
EPA uses other scientific information, including advice from its Science
Advisory Board, as well as information from affected industries andinterested members of the public, in its standards-setting process.
Comments by C&O such as ‘‘. . . the regulatory community has refused
to confront the possibility that their [regulatory] decisions were grossly inerror’’ (p. 471) are more appropriately published in OpEd articles or policy
journals. In our view, they do not belong in scientific articles that could be
used to inform regulatory standard-setting.

Acknowledgments
The Radiation Effects Research Foundation (RERF), Hiroshima andNagasaki, Japan, is a public interest foundation funded by the Japanese
Ministry of Health, Labour and Welfare (MHLW) and the U.S.
Department of Energy (DOE). The RERF research reported in thisarticle was funded in part through DOE award DE-HS0000031 to the
National Academy of Sciences. This publication was supported by RERF
Research Protocol 1-75. The views of the authors do not necessarily reflect
those of the two governments. The authors are grateful to James Cleaver,John Cologne, Evan Douple and Dale Preston for their helpful commentson this paper.
Received: January 25, 2015
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Response to Comments on ‘‘Estimating Risks of Low
Radiation Doses—A Critical Review of the BEIR VII Reportand Its Use of the Linear No-Threshold (LNT) Hypothesis’’

not consistent with LNT, and there are no apparent confounding factors,
then there is a problem with the theory. 2. We know that the LNT works
well at high doses (.100 mSv), but seems to be unable to adequately
explain all the findings when applied to doses below this threshold. This isanalogous to Newtonian mechanics and Quantum mechanics. Newtonianmechanics works well at the macroscopic level but cannot explain
behavior at the atomic level. Likewise, we contend that LNT can explain
the behavior of ionizing radiation at high doses, but fails to adequately
explain the findings at low doses. Unlike other areas of physics or science,
when a theory fails to explain the scientific data, the theory is revised andupdated. For some reason, that usual pattern is not followed in radiation
research.
The chair of the BEIR VII committee was emphatic in his support for
the LNT, with the press report going as far as recommending that people
live on the upper floors of buildings to reduce their radon exposure. We
find it hard to accept a recommendation that is so at odds with the natural
history of lung cancer. Crowley et al. claim that we failed to look at larger
studies – so let’s look at this a different way. Figure 1a is a graph of thelung cancer by county in the U.S. taken from the latest National Cancer
Institute cancer mortality map (2). Figure 1b is the graph of the radon levelby county in the U.S. taken from the EPA web site (3). Although there isno correction for potential confounding factors such as smoking, when
averaged over the entire U.S. population one would expect that these
confounding factors would be relatively constant throughout the country.
So we would anticipate a positive correlation between lung cancer andradon levels. Instead there is a clear INVERSE correlation. It is difficult toconceive how this relationship could be converted into a positive
correlation between radon level and lung cancer. Could there be
confounding factors that could convert an inverse correlation to a positive
correlation – we doubt it. Note, as one would expect, no other cancer listed
in the National Cancer Institute database demonstrates an obvious
correlation with radon level. We are not the first to point out this effect– this was first pointed out by Cohen in 1995 (4) and has been hotly
debated ever since with no clear explanation as to how these findings canbe reconciled with the LNT hypothesis (5, 6).
Thompson et al. (7) shows an example of a study that was well
performed and yet demonstrated a statistically significant radioprotective
effect at low doses of radon. The reviewers criticize us for our use of theThompson study and ignoring some larger studies. We would point out theexcellent review and comparison of the findings in the North American
study (8) vs. Thompson (7). The study of Thompson et al. is a far more
tightly controlled study than the North American studies reviewed byKrewski et al. (8), which is a meta-analysis of 7 separate studies. We havelooked at the Krewski et al. study (8) and would point out that while thisshows a positive correlation between radon and lung cancer, the methods
used to calculate risk differed significantly between the 2 studies.
Thompson allowed their data to drive the functional form of the best fit.
The study by Krewski et al. (8) fits only to a form:

Michael K. O’Connor a,1 and Edward J. Calabreseb,1aMayo Clinic, Section of Nuclear Medicine, Rochester, Minnesota 55905;
and b University of Massachusetts, Public Health, Amherst,
Massachusetts 01003

We thank Crowley et al. for their extensive comments on our recent
paper. As a general comment, we would emphasize that in the publicationof a document the size and scope of the BEIR VII report, it is not sufficient
to bury important statements on the acknowledged limitations of themethodology deep within the document. There are significant limitations
on any estimate of radiation risk (which the BEIR VII committee does
acknowledge in various parts of the report) but never mentions in either
the public or executive summary. More importantly, tables such as thosecontained in Annex 12D, which are widely used and quoted in manystudies, contain no caveats or warnings about the significant uncertainty
and limitations of the data therein. Many investigators, who are not expert
in this field, assume these calculations are based on solid scientific datarather than on hypothesis and extrapolation.

Response to Section on LNT Model and BEIR: HistoricalFoundations
While the reviewers state that ‘‘future BEIR committees . . .will not bebound by previous BEIR reports’’, the obvious tendency is to build on
previous reports. For example, the BEIR VI report (1) states ‘‘The
committee’s models are a direct extension of the BEIR IV model, which
included parameters for time since exposure and attained age, but not
exposure rate or exposure duration, as in the BEIR VI models’’. We don’t
fault the BEIR committees for doing so as it is logical to build on theknowledge base from previous reports. It does, however, make it very
difficult to break from the mold and dispassionately review alternativemodels.

Response to Section on Environmental Studies
We acknowledge that at moderate to high doses, the LNT model
provides a sound estimate of cancer risk. However at low doses webelieve that this model fails to explain the findings in many well-designed
animal and clinical studies. Crowley et al. claim that we failed to look atmany studies that support the LNT theory. To this we would like to make2 points. 1. The responders are defending a theory – the LNT. If the LNTis correct, then it should be able to explain the findings in all studies andnot ones that are preferred by Crowley et al. As the group claiming that
the LNT is unable to explain the findings at low doses, we have the luxuryof picking any study we like (provided the science is sound) anddetermining if the findings are consistent with the LNT. If the findings are
1 E-mail addresses for correspondence: mkoconnor@mayo.edu andedwardc@schoolph.umass.edu.

Odds ratio of lung cancer ¼ 1 þ Bx;
where x ¼ radon concentration and B is the excess odds ratio per unit ofradon. This is essentially a linear model that follows the LNT. To illustrate
this issue we re-plotted the summed odds ratio from all the studies shown