Assumptions Concerning Radiologic Effects

The working group’s analysis is based on the use of models of excess relative-risk (ERR).
With the inclusion of appropriate modifiers of excess risk (such as, time-since-exposure, age-at-
exposure, and attained age), these models can be used to describe the Japanese and other
radiation-epidemiology datasets. Other models, such as models of excess absolute-risk (EAR),
can also be considered but might not lead to different conclusions about AS. In fact, if these
modifiers of risk are appropriately included in the modeling, either ERR or EAR models should
function equally well for the purpose of producing AS tables.

The working group based its assumptions about the appropriate models of radiation risk
primarily on previous analyses of the atomic-bomb data by the Radiation Effects Research
Foundation (RERF) investigators. The analyses of data for solid cancers, taken together or at a
number of specific or grouped sites, Support a linear dose-response association and do not
indicate that fit would be improved by the addition of a dose-squared term. The assumption of
linearity seems Teasonable, therefore, and rests on the best available information. The atomic-
bomb study is also the best source of data on the shape of the dose-response curve for leukemia;

in this case, it supports a linear-quadratic model.

Most treatments of RBE and DDREF by radioepidemiologists and radiobiologists have
assumed that their values are constant. The working group has been innovative in introducing

uncertainty factors for these values, and this has advanced the discussion.

The NRC oversight committee (1984) noted that the treatment of extended exposures
assumed that each increment of exposure contributes additively to total risk (although possibly
with different weights related to the DDREF). That assumption is conventional, but seldom

acknowledged, in radiation epidemiology. A variety of experimental and epidemiologic data on

10


of uncertainty about the appropriate model form. Rather than rely on a priori or data-driven
decisions about what effects to include in the model (for example, whether to add a quadratic
dose-response term or age and latency modifiers), one would fit a broad suite of alternative
models and report a weighted average of their predictions—weighted by their respective
posterior probabilities. This approach has the appealing feature of providing confidence limits
that reflect the uncertainty about true model form. However, it should be noted that although
these alternative analyses will reduce the arbitrariness in the grouping of cancers to some degree,

they cannot eliminate it.

Broader Issues:

The subcommittee was not specifically charged with an evaluation of the various
compensation schemes currently in use, but some consideration of the latter is, in our view
essential, for the assessment of the proposed revision of the radioepidemiological tables. This
follows from the fact that a major purpose of the tables is to provide a means for evaluating the
merit of specific claims for compensation, and the utility of the tables therefore has to be seen in
the context of the compensation systems that they are intended to serve. Different policy
decisions have different implications for compensation, and these differences can impinge on the
utility of the tables. Accordingly, in the paragraphs that follow, we examine, albeit briefly, some
of the schemes that are in use or could be proposed to adjudicate claims for compensation and to
settle tort litigation. Our focus, however, is on improving administrative uses, such as those
pursued by the VA and the DOJ, and not judicial uses. Although the subcommittee believes that
the principles might be the same and our recommendations might be equally applicable to both
kinds of use, the legal profession was not represented on the subcommittee, and civil litigation

cases seldom turn on the value of the PC estimate alone.

31


A second language and conceptual problem is the difficulty of explaining anything related
to probability or statistics to laypersons. Helping them to understand probability often requires
use of analogies (such as, weather forecasting and dice throwing). Getting people to understand
Monte Carlo simulations will take more effort. Considerable care will be needed to translate
statistical concepts that are important for public understanding of the IREP. This problem is
made more acute by the fact that even audiences familiar with probability and statistics (such as
risk assessors) must devote considerable effort to familiarize themselves with the assumptions on

which AS is based and with the resulting choice of statistical methods.

An initial comparison of the IREP model with the 1985 tables conducted at the VA suggests
that the new model will lead to differences in compensation. It appears that using the IREP
model will make it somewhat less likely for people to be compensated than in the past. If using
the model denies more people compensation, this situation must be acknowledged in a public
summary, and the factors causing the increased claim denials should be explained. If care is not
taken, using the IREP might create serious risk communication problems for the VA, with the

agency being accused of applying it only to save money and not to assist veterans.

The National Cancer Institute (NCT) has suggested that the program (IREP) and the public
summary be made available on its Web site. The subcommittee endorses NCT's efforts to make
both more accessible to potential users. However, putting the program (IREP) on a Web site so
that lay people can use it presents a number of communication questions that must be evaluated
in depth. First, directions on how to use the program will have to be readily understandable.
Second, the various radiologic and statistical concepts involved must be carefully explained;
computer graphics and animation might be helpful, but expensive. Third, and most important, it
will be essential to provide context for and explanations about how to interpret the final numbers
coming out of the model and what they mean for an individual, from both health and

compensation perspectives.

27


because it equals the LLE in the total population divided by the total risk, which we are assuming
is different between the two populations. A policy based on either the PC or the LLE would
therefore correctly favor the low-risk population but would do so differently: a PC-based policy
would reward a higher proportion of cases, but the size of the award would be the same for
compensable cases in both populations; an LLE-based policy would reward all cases in both
populations, but the average size of award per case would be greater in the low-risk population,

Which is a more equitable solution is an important policy decision.

The subcommittee views those conclusions as worthy of more careful consideration by
agencies responsible for establishing compensation and tort-litigation policy. We recognize,
however, that the current policy is grounded on the notion of AS, and we do not wish to discredit
the efforts of the working group by implying that its estimates are not useful as a basis for
resolving such claims, even in the face of fundamental problems of interpretation. The
subcommittee suggests that the appropriate branches of government consider, whether
compensation policy should be altered in favor of the use of proportional payment schemes,

explicit consideration of uncertainties, and the use of criteria based on LLE rather than PC.

Role of Uncertainty in Compensation:

As previously stated, the working group is to be commended for its careful consideration of
uncertainty in the estimates of AS. Questions remain, however, about how to handle the
remaining components of uncertainty that cannot be quantified and about how they should be

addressed in resolving compensation claims.

With regard to residual uncertainty, it should be noted that the problem has been lessened by
extensive efforts to quantify many components of uncertainty and to reduce what remains. It

would be helpful if the authors were more explicit in what types of uncertainty should be

36


At the meeting on May 16-17, 2000, the subcommittee heard presentations by members of
the working group charged with the revision, specifically, Charles Land and Ethel Gilbert, of the
National Cancer Institute, and Owen Hoffman, Julian Apostoaei, and Brian Thomas of SENES
Oak Ridge, Inc. The presentations dealt with the strategy adopted in the revision of the tables,
the demonstration of the Interactive Radio-Epidemiological Program (IREP) developed for the
computation of assigned share (AS), and specific portions of the Program and the default values.


Table 2. Random-Effects Analysis of Sites in Thompson et al. (1994) Table X

Thompson et al. Analysis Random-Effects Analysis
Posterior estimates

Site ERR/Sv Lei Uci ERR/Sv Lei Uci
Oral 0.29 -0.09 0.93 0.42 0.14 0.70
Esophagus 0.28 -0.21 1 0.43 0.13 0.73
Stomach 0.32 0.16 0.5 0.35 0.19 0.51
Colon 0.72 0.29 1.3 0.55 0.27 0.84
Rectum 0.21 -0.17 0.75 0.38 0.11 0.66
Liver 0.49 0.16 0.92 0.48 0.23 0.74
Gallbladder 0.12 -0.27 0.72 0.36 0.08 0.64
Pancreas 0.18 -0.25 0.82 0.39 0.10 0.68
Respiratory 0.8 0.5 1.2 0.64 0.39 0.88
Trachea 0.95 0.6 1.4 0.68 0.42 0.94
Skin 1 0.41 1.9 0.57 0.26 0.88
Prostate 0.29 -0.21 1.2 0.44 0.14 0.75
Bladder 1 0.27 21 0.54 0.23 0.86
Kidney 0.71 -0.11 2.2 0.50 0.17 0.82
Nervous

system 0.26 -0.23 1.3 0.44 0.13 0.75

64


policy implications which should be considered by the appropriate agencies that would use

these tables.

The subcommittee notes that the AS calculation can be a biased estimate of personal PC
when there is heterogeneity in baseline rates of cancer. We do not, however, adopt the
position that one can expect extreme heterogeneity in baseline rates a priori. Analysis of
monozygotic (MZ) and dizygotic (DZ) twins’ data, for example, tends to show important, but
far less than complete, dependence of risk of cancer on either genes or shared environment.
Simulation studies indicate that even large variations in baseline rates lead to a high

correlation between AS and personal PCs.

Recommendations

e

The subcommittee suggests that tumors without demonstrated radiogenicity be analyzed in
three ways and that the results of the three analyses be pooled to assign an ERR estimate for
each diagnosis: as a single combined group with a single dose-response relation fitted to the
occurrence of all such tumor; as a part of the group of all solid tumor; and combined with
anatomically similar cancer sites. Uncertainty analysis should reflect the uncertainty in
assigning ERR estimates so obtained to specific diagnoses. More generally, we suggest

future consideration of hierarchical methods for estimating dose-response relations.

The subcommittee recommends that AS calculations be performed for nonmelanoma skin
cancers; information regarding the baseline rates in the United States is needed to use the
transportation algorithm described in the working group’s report. These skin cancers are not
reportable in the United States, so routine SEER data are unavailable; but various surveys
provide sufficient information for transportation (perhaps incorporating uncertainty into the

baseline US rates) to be attempted.

46


Assumptions Concerning Radiologic Effects

Conclusion

The working group has used assumptions concerning radiologic effects that reflect the
judgment of authoritative groups on radiation risk assessment. The subcommittee supports

its approach and choices,

Recommendations

The dose-response coefficients and their confidence limits for each tumor type are key
elements of the AS assessment, The working group’s report should include a table to provide

this information.

The subcommittee recommends that the working group’s report include a summary table of
each radiologic assumption, including a brief, nontechnical description, central estimates of

the factors, and uncertainty parameters.

It is recommended that a summary table be developed to compare the 1985 and 2000 AS
results for a number of scenarios and tumor types. It should be accompanied by text that

explains the main sources of differences between the two sets of results.

In many compensation settings (such as worker exposures), it might not be clear for a given
measured exposure whether the acute- or protracted- (chronic) exposure option should be
used (or whether it should be a mixture of the two). The working group needs to provide
more information on the use of the chosen option and might want to consider providing an

option to model a mixture of acute and chronic exposures for a given measurement.

44


site better. In the interim, to increase the sample size, the working group could consider
grouping all these sites for a single analysis or combining each of the sites with its larger
organ system. The subcommittee notes that this Suggestion also has relevance to the issue of

uncertainty in the estimates of PC (or AS).

¢ To make it easier to calculate AS for a set of subjects or exposure scenarios, the
subcommittee suggests that IREP be modified to allow batch processing. If that suggestion
is implemented, a user could create a file in which each record reflects the characteristics of a
single subject and his or her exposure history. The program would use this file as input,
would process all records in the file, and would create a tabular output file reporting selected

percentiles of AS, components of AS, and components of the adjusted ERR per sievert.

Making the Radioepidemiological Tables Available to the Public

Conclusions

e The revised tables have the potential to be more useful and accessible to a broader audience
than the existing tables. Nonetheless, it would be unfortunate if the working group sacrificed

any of the benefits of the current tables.

© The IREP program is an important first step in making the information in the revised tables
more broadly and effectively available and useful.

* A purely technical presentation of the revised tables will be inadequate to meet the needs of
all potential users. In its current State, the program cannot be effectively used by persons
who lack familiarity with computers or with the calculation of AS. A novice in either case

would have difficulty navigating the program.

¢ Distribution of software, whether on the Web or by other means, imposes specific additional
requirements for effective dissemination. In particular, the software must explicitly address

the issues of availability, transparency, flexibility, and suitability for its likely audiences.

50


¢ The PC-based approach to compensation takes no account of the loss of life expectancy
experienced by claimants at the stage of deciding whether to make an award, but only
perhaps at the stage of choosing the magnitude of the award once such a decision has been

reached.
Several alternatives have been suggested, including the following:

¢ The use of payment schemes on a sliding scale, such as the one that has been used by British

Nuclear Fuels Ltd for over a decade.

¢ Compensation schemes based on the estimated loss of life expectancy attributable to

exposure.

¢ Consideration of the distribution of uncertainty in reaching decisions.

The subcommittee suggests that such alternatives be considered by the appropriate agencies.
In other words, the subcommittee does not advocate a specific compensation system, but

suggests that agencies involved in compensation claims review the systems that they use to avoid

inequities in their procedures.

54


The subcommittee believes that some allowance for uncertainty is appropriate once a
population association is established, particularly if an agency has a stated policy to give
claimants the benefit of doubt. However, even under a policy that aims at equity in that there
should be a reasonable balance between “false-positive” and “false-negative” decisions, we
believe that uncertainty needs to be considered by using the full probability distribution produced
by uncertainty-propagation methods like that used in IREP. For example, one might consider a
scheme that pays in proportion to the posterior probability that PC is greater than 50% or in
proportion to the expected value of PC. Such schemes tend to be more equitable than simple all-

or-nothing decisions based on the point estimate of PC or some upper confidence limit.

42


5. ASSESSMENT OF TIME-SINCE-EXPOSURE AS A MODIFIER OF RISK

With respect to modification of risk by time-since-exposure (TSE), it is unclear why the
working group’s analyses were limited to linear trends in TSE (except for leukemia). There are
precedents for nonlinear trends: that between radon and lung cancer and that between external
radiation and thyroid cancer. In the latter case, there is evidence of a nonlinear TSE effect in
which the risk first increases then decreases. Nonlinear TSE trends are also compatible with a

multistage model wherein an intermediate stage is affected by the exposure.

14


Usefulness of Probability of Causation:

The 1984 National Research Council Oversight Committee noted that the so-called
probability of causation was strictly speaking not a probability, but rather an estimate of the
Proportion of cancers that were caused by exposure in a large (hypothetical) group of similarly
exposed cases—an estimate that was then assigned to all members of the group. Therefore, the
committee recommended the use of the term assigned share for this quantity, and this
recommendation was adopted by the working group in the current revision, One of the reasons,
the National Research Council Oversight Committee chose assigned share over probability of
causation was the possibility of a hazard ratio less than one. However, in the present instance,
the working group used only models for which the hazard rate for the exposed group is always
greater than the hazard rate for the unexposed group. That ensures a positive numerator in the
standard PC formula and guarantees that the expression is a probability. Given that fact and that
the literature on probabilistic causality (see Pearl, 2000 for a review) generally uses probability
of causation or a probability of a cause, a term traceable to Laplace (1774), we have used
probability of causation and assigned share as essentially synonymous in this context. Pearl
(2000) discusses the conditions for identification of necessary and sufficient causes and
illustrates the concepts with an example of the effect of radiation on leukemia. But the
subcommittee wishes to explore some questions related to the concepts as statements of causality

somewhat more deeply.

Since the publication of the original radioepidemiological tables, a growing statistical
literature has called into question the validity of not only the PC, but even the AS concept
Greenland, 1999; Beyea and Greenland, 1999; Greenland, In press). For example, Robins.and
Greenland (1989) have derived bounds for population averages of PC for heterogeneous
populations and studied the effect of independent and dependent censoring on these bounds. It
can be claimed, however, that population averages are not of interest in the compensation of a

particular individual (Groer, 1985). Robins and Greenland (1989) have also argued that in the
32


APPENDIX C

Supplement to Text

The following two tables and the software supplement the text in chapter 10 “Future

Revisions and Broader Issues”, section “Role of Uncertainty in Compensation”.

Table 1. Summary of Table X (from Thompson et al., 1994)

Site ERR/Sv Std Err
Oral 0.29 0.255
Esophagus 0.28 0.3025
Stomach 0.32 0.085
Colon 0.72 0.2525
Rectum 0.21 0.23
Liver 0.49 0.19
Gallbladder 0.12 0.2475
Pancreas 0.18 0.2675
Respiratory 0.8 0.175
Trachea 0.95 0.2
Skin 1 0.3725
Breast 1.6 0.275
Uterus -0.15 0.0975
Ovary 0.99 0.545
Prostate 0.29 0.3525
Bladder 1 0.4575
Kidney 0.71 0.5775
Nervous system 0.26 0.3825
Thyroid 1.2 0.405

63


COMMITTEE ON AN ASSESSMENT OF CENTERS FOR DISEASE CONTROL AND
PREVENTION RADIATION STUDIES FROM DOE CONTRACTOR SITES:

SUBCOMMITTEE TO REVIEW THE RADIOEPIDEMIOLOGY TABLES

WILLIAM J. SCHULL (Chair), Emeritus Professor, University of Texas-Houston, Houston, TX
SHARON M. FRIEDMAN, Lehigh University, Bethlehem, PA

PETER G. GROER, The University of Tennessee, Knoxville, TN

SUSAN E. LEDERER, Yale University School of Medicine, New Haven CT

ROY E. SHORE, New York University Medical Center, New York, NY

DANIEL O. STRAM, University of Southern California, Los Angeles, CA

DUNCAN C. THOMAS, University of Southern California, Los Angeles, CA

DANIEL WARTENBERG, Robert Wood Johnson Medical School, Piscataway, NI

JOHN S. YOUNG, The Hampshire Research Institute, Alexandria, VA

CLS ADVISER
BARABARA S. HULKA, University of North Carolina at Chapel Hill, NC

NATIONAL RESEARCH COUNCIL STAFF
ISAF AL-NABULSL, Study Director
BRIDGET R. EDMONDS, Project Assistant
DORIS E. TAYLOR, Staff Assistant

SPONSOR’S PROJECT OFFICER
JAMES SMITH, Centers for Disease Control and Prevention

EDITOR
NORMAN GROSSBLATT

iv


LEV. PARAMETER (NCONV) ESTIMATE Ss, ERROR(U) PREV. ESTIM CORR.

1 CONS /CONS (1) 0.02878 0.02942 0.0288
fixed

PARAMETER ESTIMATES. ERROR(U) PREV. ESTIMATE
CONS 0.4798 0.07837 0.4798
note -- use model results to form reliability coefficient rho
calc c41=0,02978+c4*c4

calc c41=0.02978/c41

names c41 'rho'cl1 ‘predict’

note -- form predictions based on estimated err, and rho
calc c11="'thoTM'rr'+(1 -'tho')*0.4798

note -- form variance of model predictions

note -- as var(rand_eff)*(1-rho)+var(fixed effect)

calc c21=0.0279*(1-'tho')}+0.02942*0,02942

cale c21=sqrt(c21)

names C10 'RandEff c11 'Predict' c20 'VarRE!

names c21 'stdev_p'

print ‘lineno' 'Predict' 'stdev _p''rr' ‘stderr!

LINENO PREDICT STDEV_P RR STDERR
N= 15 15 15 15 15

1 2.0000 0.42018 0.14143 0.29000 0.25500
2 4,0000 0.43074 0.14804 0.28000 0.30250
3 5.0000 0.35120 0.079453 0.32000 0.085000
4 6.0000 0.55627 0.14101 0.72000 0.25250
5 7.0000 0.38262 0.13681 0.21000 0.23000
6 8.0000 0.48441 0.12710 0.49000 0.19000
7 9.0000 0.36210 0.14014 0.12000 0.24750
8 10.000 0.39170 0.14341 0.18000 0.26750
9 11.000 0.63766 0.12252 0.80000 0.17500
10 12.000 0.68047 0.12984 0.95000 0.20000
11 13.000 0.57172 0.15439 1.0000 0.37250
12 17.000 0.44311 0.15288 0.29000 0.35250
13 19.000 0.54459 0.15903 1.0000 0.45750
14 20.000 0.49867 0.16272 0.71000 0.57750

15 21.000 0.44263 0.15507 0.26000 0.38250

note now do likelihood ratio test for nonzero variance
like 249891 spaces left on worksheet

-2*log(Ih) is 7.70057
dinput to c41

66


MLN - Software for N-level analysis.

Note --- read in data
dinp to cl-c4 .
250000 spaces left on worksheet

Type file name
>

solid.dat
120.29 0.255
140.28 0.3025
15 0.32 0.085

1 6 0.72 0.2525
17 0.21 0.23
180.49 0.19
190.12 0.2475
1 10 0.18 0.2675
1110.8 0.175
1120.95 0.2
113 10.3725
117 0.29 0.3525
119 10.4575

1 20 0.71 0.5775
1 21 0.26 0.3825

name cl ‘cons' c2 ‘lineno’ 3 'rr' c4 'stdert’

iden 1 ‘lineno’
iden 2 ‘cons’
expl ‘cons’
resp ‘rr’

setv 1 ‘cons’

note --- set variance offset (var of estimate of err

offset 1 from c4
note setv 2 ‘cons’
batch 1

Batch mode is ON
tole 3

maxit 20

note -- fit model
start

Convergence achieved
note -- display model results
rand

65


REFERENCES

Beyea J, Greenland S. The importance of specifying the underlying biological model in
estimating the probability of causation. Health Phys 76:269-274, 1999,

Carlin BP, Chib S. Bayesian model choice via Markov chain Monte Carlo. JRSS B 57:473-84,
1995.

Carlin BP, Louis TA. Bayes and Empirical Bayes Methods for Data Analysis. New York:
Chapman and Hall, page 3, 1996.

DOJ (Department of Justice) Final Report of the Radiation Exposure Compensation Act
Committee. Submitted to Human Radiation Interagency Working Group. Department of
Justice, 1996.

DerSimonian R, Laird N. Meta-analysis in clinical trials. Controlled Clinical Trials 7: 177-188,
1986.

Fears TR, Scotto J. Changes in skin cancer morbidity between 1971-72 and 1977-78. J Nat
Cancer Inst 69:365-370, 1982.

Glass AG, Hoover R. The emerging epidemic of melanoma and squamous cell skin cancer. J Am
Med Assoc 262:2097-2100, 1989,

Greenland S. A semi-Bayes approach to the analysis of correlated multiple associations, with an
application to an occupational cancer mortality study. Stat Med 11:219-30, 1992.
Greenland S. Methods for epidemiologic analyses of multiple exposures: A review and

comparative study of maximum likelihood, preliminary-testing, and empirical Bayes

Tegression. Stat Med 12:717-36, 1993,

68


Among the key system assumptions not editable by users in the ANALYTICA browser

and potentially in need of additional justification or an option for user modification are these:
e ERR/Sv cumulative probability tables for organ systems (starting data; ERR‘Sy data).
. Rank-order correlation for multiple exposures (Correlated ERR/Sv).
e Adjustments for latency (ERR/Sv latency).
e Truncation (at zero; ERR/Sv truncated).

Distribution form and parameters for errors in dosimetry (see page 32 of working

group’s draft report).

e Gamma and neutron bias and neutron uncertainty (systematic errors in dosimetry;

Bsed).

Users can modify the sampling approaches used in the Monte Carlo analysis. This option
could have an important and possibly self-serving impact on the results that are generated. If this
is unintentional, the working group must disable the relevant selection and explain why the
particular option (median Latin hypercube sampling with “minimal standard” randomization) is
preferable to altemative methods commonly used in Monte Carlo analysis or provide users with
a clear explanation of the rationale for selecting among the available options and the

consequences of selections for the resulting prediction at different Monte Carlo sample sizes.

A similar issue concerns the selection of sample sizes. The default is set at 1,000, which

does not yield particularly stable predictions of the 99th percentile AS value in repeated runs for

25


low-LET radiation indicate that the risk posed by highly fractionated or protracted exposures is
not greater than, and even tends to be less than, that posed by an acute exposure of the same total
magnitude. The subcommittee therefore agrees that the assumption of additivity is reasonable
and that there is no need to build into the model a potentiation of effects by serial exposures.
However, the working group should provide a fuller justification in its report for assuming
multiple or protracted exposures are additive in their effects and the impact on AS estimates if

this assumption is incorrect.

A brief summary of what is known about additive or interactive effects of exposure to
radiation and other toxic agents (besides cigarette smoke) would also be informative for the
broader scientific and user communities. In particular, users might wonder whether a person’s

radiation exposure potentiated the effects of other toxic agents to which he or she was exposed.

Modeling and Choice of Parameters

The working group chose to base its AS calculations on separate analyses of the Japanese
atomic-bomb survivor data on each of 33 sites of cancer. That is justified by the fact that each
cancer type is a distinct disease, with different biologic processes that lead to potentially
different degrees of radiosensitivity or modification by age, sex, latency, and other factors. The
possible heterogeneity among cancer sites must be weighed against the smaller number of
cancers available for analysis, as subdivision of tumor types becomes increasingly fine with a
consequent increase in uncertainty in parameter values and lower power to effects of modifying
factors. At the same time, the upper-bound confidence limits on calculated probability of
causations (PCs) might become so large that no cases could be excluded as radiogenic, no matter
how low the dose. The subcommittee believes that for the major cancer sites, the working group
has struck a sensible balance—allowing for some differences between cancer sites but not

subdividing the data more finely than is warranted. In the case of some of the rarer sites,

11


8. TREATMENT OF UNCERTAINTY

Monte Carlo Simulation

In developing its computer program (IREP) to calculate the probabilities of causation, the
working group used Monte Carlo simulation to model uncertainties in the estimates. To help
readers appreciate this change in method, it would be useful for the report to include a
comparison of the methods used to account for the uncertainties in the 1985 tables and in IREP.
This comparison should include assumptions of methods used, data needs, and improvements
achieved by the new method. This comparison should also include some discussion of where the

two sets of tables differ most.

In the original tables, the authors identified a series of parameters to which they assigned
uncertainty distributions: baseline values, influence of age-at-exposure, time-since-exposure,
ratio of linear coefficients in the linear and linear-quadratic dose-response models, latent period,
and risk coefficients. They assumed that each uncertainty distribution was lognormal and
specified a geometric standard deviation (GSD). To propagate the- individual uncertainties and
calculate the GSD for the overall uncertainty distribution, S, they combined the individual GSDs,

Sj, into an overall GSD by using this equation:

(In (S))? = (in (Sp)? +... + (in (SW)?

That resulted in a lognormal distribution of uncertainty about the estimated probability of
causation. This method of combining individual uncertainties has limitations: the distributions
are assumed to be lognormal and the appropriateness of this assumption is uncertain, the
distributions can be combined only through addition of the logarithms of the GSDs, and the

GSDs have to be estimated for each parameter.

17


4. TREATMENT OF ACUTE AND PROTRACTED (CHRONIC) EXPOSURES

The level of detail incorporated in the working group’s report and in the model pertaining to
acute, fractionated, and protracted (chronic) exposures seems disproportionate to the amount of
information in the average occupational record. Few records contain more than weekly exposure
estimates, and most have only monthly or quarterly measurements or yearly summaries. It
therefore seems unlikely that the acute-chronic choice in the IREP can be useful. Instead, the
officials responsible for adjudicating compensation claims might routinely assume that the
exposure aggregated over the smallest unit of time available (such as a quarter-year) was
received as a single acute dose to provide a liberal estimate of risk. The working group’s report

should discuss the situations with limited information on exposure and provide recommendations

on how to use the program in such cases.

Many users might not understand the rationale for or implications of using the acute-chronic
option, and a clear explanation should be provided that is appropriate for the types of exposure

information likely to be available.

13


at least some cancer sites (a 10-fold increase in sample size yielded increased stability, albeit at a
substantial cost in processing time). The working group needs to document the results of a

parametric examination of alternative default options as part of its internal testing of the

software.

The design team deserves considerable praise for its efforts, not only with regard to its
specific mandate, but also with regard to a broader understanding of the policy context of that
mandate. The interactive software offers many advantages over the tables that it supplements,
and there has clearly been careful thought about the implementation of the specified analysis.
However, considerable development work is warranted before this product will be suitable for

any but highly specialized audiences.

b) Other Ethical and Communication Issues:

Informing the public about the IREP will be difficult but necessary. A number of
communication issues present themselves, including language and conceptual problems. One
language problem involves explaining differences among risk, PC, and AS. For example, a
member of the public might try to run the PC calculation for a cancer that has not yet occurred
and misinterpret the resulting value as the probability that it will occur in the future. Although
using the term PC might provide serious technical problems in the IREP, PC might be more
understandable to laypersons than AS because many people are familiar with the words
probability and causation in the context of health and environmental risks. Explaining AS, a
concept unfamiliar to many, will be more difficult. That is not to suggest that AS not be used as
the prime term in the IREP, but it is worth recognizing that AS is not well understood by most
laypersons. When the IREP information is communicated to the public, attention should be paid

to explaining AS in-some detail—not just defining it.

26


The uncertainties in random errors in dosimetry were based on the main analyses of the
dose-specific risk factor in the atomic-bomb study. The source of data used to model
uncertainties in the relative biological effectiveness (RBE) of neutrons or to model systematic
bias in the gamma and neutron dose estimates is unclear; one would have to review National
Council on Radiation Protection and Measurements (NCRP) Report 126 (NCRP 1997) to
determine this. It is recommended that the working group elaborate on these uncertainties in its

report.

Estimation of the DDREF for low-LET radiation rests, in part, on data summaries and
judgments by the International Commission on Radiological Protection (ICRP), NCRP, and the
Environmental Protection Agency (EPA), but uncertainty distributions and modeling of the
DDREF were apparently based on the working group’s own judgment. That seems reasonable
and preferable to the use of a single DDREF value. However, the working group should state
how it derived the uncertainty distributions for the DDREF.

Estimates and uncertainties of the RBE values for high-LET radiation were based on the
most comprehensive and authoritative summaries available. The large uncertainty factors used

for the RBE values are appropriate, given the diverse results found in experimental studies.

The transfer of radiation risks from the Japanese population to the US population was
handled by Monte Carlo simulation to determine the average of the risks derived with additive
and multiplicative models. The use of a trapezoidal distribution appears to be a defensible way
to do this. The transfer used the atomic-bomb risk coefficients and the comparative baseline
rates of cancers in the United States [Surveillance, Epidemiology, and. End Results (SEER

Registry)] and the atomic-bomb-unexposed group.


249891 spaces left on worksheet

Type file name
>

small.txt
1.001

rcon c41

maxit 100
next

Convergence achieved

like

249904 spaces left on worksheet
-2*log(th) is 10.0264

stop

67


distribution, but also user-friendliness. Planning for this dissemination of the program and
the revised tables should include adequate representation of lay stakeholders; making the
program’s operation more transparent to the user (the documentation described in the
preceding recommendation can serve as the basis for this); and ensuring flexibility of the
program to accommodate user input for variables and procedures not fixed by law, policy, or

scientific consensus and identifying the ones that are so fixed.

The use of the Web to disseminate and support the revised tables needs to be carefully
considered. If the working group elects to pursue this option, guides should be constructed
for audiences that do not regularly access the Web, a non-Web means of notifying potential
audiences about the Web site and alternative access mechanisms should be developed, an
adequate plan and budget for technical support of the Web site and other mechanisms must
be developed if Web distribution is to succeed, and great care will be needed to address the

privacy concerns of users if the program is implemented as an interactive Web application.

The computer software ought to inform the user that his or her cancer could be one of the
cases that make up the large natural incidence of cancer in the United States, one of the cases

of that cancer due to other causes, induced by medical radiation, or induced by natural

background radiation.

52


CONTENTS
EXECUTIVE SUMMARY
1. INTRODUCTION.
2, SUMMARY OF THE SUBCOMMITTEE'S REPORT
3. CRITIQUE.
Utility of the Data Sources
Data Sources for Estimates of Uncertainties .
Assumptions Concerning Radiologic Effects
Modeling and Choice of Parameters.
TREATMENT OF ACUTE AND PROTRACTED (CHRONIC) EXPOSURES ..
ASSESSMENT OF TIME SINCE EXPOSURE AS A MODIFIER OF RISK ....
APPLICABILITY OF ASSIGNED SHARE CALCULATIONS TO MINORITY GROUPS.
ESTIMATION OF ASSIGNED SHARE FOR NONMELANOMA SKIN CANCER.
TREATMENT OF UNCERTAINTY .
Monte Carlo Simulation ..
Residual Uncertainties and Use of a Scale Factor ....
9. HOW SHOULD THE RADIOEPIDEMIOLOGICAL TABLES BE MADE AVAILABLE TO THE PUBLIC
Evaluation of the Interactive Radio-Epidemiology Program.
Accessibility.
Transparency
Flexibility
Other Ethical and Communication Issue:
10. FUTURE REVISIONS AND BROADER ISSUES .
Future Revisions
Broader Issues ..
Usefulness of Probability of Causation.
Role of Uncertainty in Compensation
11. CONCLUSIONS AND RECOMMENDATIONS .
Utility of Data Sources
Conclusion ...
Recommendations .....
Assumptions Concerning Radiologic Effects
Conclusion ...
Recommendations ..
Epidemiologic and Biostatistical Models
Conclusions
Recommendations
Handling of Uncertainty.
Conclusions...
Recommendations
Suggestions...
Making the Radioepidemiological Tables Available to the Public.
Conclusions
Recommendations
Suggestions.
12. SUGGESTIONS FOR FUTURE COMPENSATION POLICY
13. APPENDIX A: SPECIFIC COMMENTS ON THE WORKING GROUP'S DRAFT REPORT.
14. APPENDIX B: SPECIFIC COMMENTS ON THE INTERACTIVE RADIO-EPIDEMIOLOGICAL PROGRAM
Installation
ANALYTICA Constraints.
IREP Choices in Implementation
Input Data.....
15, APPENDIX C: SUPPLEMENT TO TEXT.
16. REFERENCES...
17, INFORMATION ON COMMITTEE MEMBERS ..

eI Ae



One unnecessary restraint on system access is how identification information and other
personal information are collected by the system. This may be of concem to potential users,
particularly if a Web-based implementation is chosen. For example, there is no need to collect
the name of the person being evaluated, and this field should be made optional. More important
is the specification of birth year and year of diagnosis as fundamental data items. The important
variables to collect are age at each exposure and age at diagnosis of each cancer. There is no
need to collect data on calendar years for this, although it offers convenience for data entry; this
convenience needs to be balanced against a large perceived threat to privacy, particularly if
someone is using a Web site and distrusts the government. An option for different ways to enter

time information would address this concern.

Transparency:

The transparency of IREP could be considerably improved. The “Help” function, for
example, calls ANALYTICA help, rather than model help. The latter can be accessed by a series
of context-specific buttons, but it is not easy to move among topics. A standard Windows help
file would be better. It might be possible to let users have a choice of help files in the help

window.

More important, the program (IREP) does not adequately convey the nature of the problem
that it is designed to address, nor does it distinguish the analysis that it provides from other kinds
of analysis to prevent misunderstanding by inexperienced users. At a gross level, the program
needs to warn users that the information it provides cannot be used in the prediction of cancer
risk. At a more refined level, it should clearly explain the underlying assumptions of the analysis

and some of their consequences. Section 10 on “Broader Issues” identifies some places where

23


APPENDIX B

Specific Comments on the Interactive Radio-Epidemiological Program

Most of the following comments deal with the potential for making improvements in the
impressive start made by the design team. Sponsors will need to consider not only the policy, but

also the budgetary, implications of these suggestions.

Some of the concerns of the subcommittee set forth here might reflect the design team's
decision to implement the model in the proprietary modeling-spreadsheet program
ANALYTICA, rather than coding it directly. We recognize that there are savings in
development expense if one uses this type of environment. However, the savings often mean
that the model developer has less flexibility in preparing the user interface. Accordingly, some
of our suggestions, particularly regarding system appearance and convenience, could be too
difficult or too expensive to implement. Ultimately, their value depends on the size and nature of
the user base. As stated above, the information supplied by the developers indicates that the
primary target audience is small and familiar with the underlying logic of the system. However,
careful consideration needs to be given to other potential audiences, particularly those for whom
the process is not well understood but who have a substantial stake in the outcome. The
developers clearly were aware of this design consideration, but the design as implemented is
only partially successful in addressing the broader stakeholder community. In particular, key
features of a system to meet stakeholder needs might be difficult to implement if only the

"browser" version of ANALYTICA is available to users.

Some of the ensuing comments reflect the interim nature of the development system and

will not necessarily apply to a final implementation.

59


The cursor is uninformative about what one can and cannot operate: some things look

like jumps but do not go anywhere.

IREP Choices in Implementation

The use of an edit table for names and reference number is not particularly intuitive.
Also, the system is not apparently creating any kind of index of multiple runs with these data. If
these were only labels, an editable text box would give the user an easier job, more flexibility,

and less concern.

Year edits are inconvenient and counterintuitive. A count-up and count-down boxes
would make the user's life easier. Hitting <Enter> (the most intuitive keystroke after typing a
value), rather than registering the user's choice instead adds a blank line to what should be a one-

line field.

Edit tables carry information from the previous case instead of zeroing out when starting
anew case. By entering a male who was born in 1952 with diagnosis in 2000 and indicating five
exposures, the system preselected 1955 as the first exposure. Also, edit tables are not closed by
the clicking of the green check (an expected outcome for users of this icon). Again, the
transposition options on edit tables are of uncertain value; they seem to add considerable visual

complexity without addressing a user need.

Input Data

Note the discussion of date issues under “Accessibility”. Working on age rather than

year would eliminate a user entry.

Dose-input information might be the least user-friendly aspect of the system, particularly
if distributional data are expected to be the norm. The user should not be asked to type ina
distribution name and parameters, but rather at a minimum should be offered a drop list and

count-up and count-down boxes for parameters. Also, it seems awkward to have to provide the

61


doses and dates on separate tables, inasmuch as when the two would naturally be lined up

together in a single table.

Serious consideration needs to be given to both the numerical and physical forms of the
data received by expected users. Will the people responsible for adjudicating compensation
claims for radiation-related cancer have dose information that describes a particular distribution,
or are they expected to infer one? Do the data come in on paper or electronically? Related to
this problem is the absence of error checking on data entry. The user gets well into the analysis

before the system crashes with bad data.

62


additional, miscellaneous factor might not be necessary. On the other hand, its inclusion
provides assurance that the calculation favors approval of possibly meritorious claims by

affected people.

We recognize that the IREP formulation is not exhaustive in its inclusion of factors that
contribute uncertainty to the estimate of overall risk. However, we believe that it is
inappropriate to include an uncertainty distribution for unspecified factors and that it is not
possible to specify a distribution for such factors, In place of the current general scale factor, we
recommend an option to enable users to include a factor in addition to those explicitly stated,
provided that they specify a name and a distribution. To implement that option, the general scale
factor should be renamed “additional uncertainty factor”, its default GSD should be set to 1.0 so
that it will have no effect unless altered by the user, and a user who wishes to include this factor,
should first have to specify a name for the uncertainty distribution (that is, identify specifically
what variation the factor represents) and then specify the GSD. It is important that the strongest

possible justification is set forth for the choice of the GSD when this factor is used.

To summarize, the Radioepidemiological Tables Working Group is to be commended for its
careful consideration of uncertainties in the estimates of AS. These uncertainties range from
Statistical sampling errors in the estimated relative-risk parameters that can be directly computed
from the data to a variety of potential biases and sources of uncertainty (such as measurement
error, dose-rate effects, and risk-transport problems) where some data exist to suggest a
reasonable range of values to a miscellaneous category of other uncertainties for which it is
impossible to justify any particular choice of values. The subcommittee generally endorses the
methods that have been used to address the first few sources of uncertainty and the specific

distributional parameters that have been adopted.

20


Shore R, Albert R, Reed M, Harley N, Pasternack B. Skin cancer incidence among children
irradiated for ringworm of the scalp. Radiat Res 100:192-204, 1984.

Thomas DC. Pro: The probability of causation can be used in an equitable manner to resolve
radiation tort claims and design compensation schemes (Abstract). Radiat Res, In press,

Thomas DI, Salmon L, Antell BA. Revised technical basis for the BNFL/UKAEA compensation
agreement for radiation linked diseases, J Radiol Prot 11:111-116, 1991.

Thompson DE, Mabuchi K, RonE, Soda M, Tokunaga M, Ochikubo S, Sugimoto S, Ikeda T,
Terasaki M, Izumi S, Preston DL. Cancer incidence in atomic bomb survivors. Part II:
Solid tumors, 1958- 1987.” Radiat Res 137(2 Suppl): S17-67, 1994.

Witte JS, Greenland S, Haile RW, Bird CL, Hierarchical regression analysis applied to a study
of multiple dietary exposures and breast cancer. Epidemiol 5:612-21, 1994,

Wong FL, Boice JD Jr, Abramson DH, Tarone RE, Kleinerman RA, Stovall M, Goldman MB,
Seddon JM, Tarbell N, Fraumeni JF Jr, Li FP. Cancer incidence after retinoblastoma:

radiation dose and sarcoma risk. JAMA 278:1262-1267, 1997.

72


COMMISSION ON LIFE SCIENCES

MICHAEL T. CLEGG (Chair), University of California, Riverside, CA

PAUL BERG (Vice Chair), Stanford University School of Medicine, Stanford, CA
FREDERICK R. ANDERSON, Cadwalader, Wickersham & Taft, Washington, DC
JOANNA BURGER, Rutgers University, Piscataway, NJ

JAMES E. CLEAVER, UCSF Cancer Center, San Francisco, CA

DAVID S. EISENBERG, University of California, Los Angeles, CA

NEAL L. FIRST, University of Wisconsin, Madison, WI

DAVID J. GALAS, Keck Graduate Institute of Applied Life Science, Claremont, CA
DAVID V. GOEDDEL, Tularik, Inc., South San Francisco, CA

ARTURO GOMEZ-POMPA, University of California, Riverside, CA

COREY S. GOODMAN, University of California, Berkeley, CA

JON W. GORDON, Mount Sinai School of Medicine, New York, NY

DAVID G. HOEL, Medical University of South Carolina, Charleston, SC
BARBARA S. HULKA, University of North Carolina at Chapel Hill, NC
CYNTHIA J. KENYON, University of California, San Francisco, CA

BRUCE R. LEVIN, Emory University, Atlanta, GA

DAVID M. LIVINGSTON, Dana-Farber Cancer Institute, Boston, MA
DONALD R. MATTISON, March of Dimes, White Plains, NY

ELLIOT M. MEYEROWITZ, California Institute of Technology, Pasadena, CA
ROBERT T. PAINE, University of Washington, Seattle, WA

RONALD R. SEDEROFF, North Carolina State University, Raleigh, NC
ROBERT R. SOKAL, State University of New York, Stony Brook, NY
CHARLES F. STEVENS, The Salk Institute for Biological Studies, LaJolla, CA
SHIRLEY M. TILGHMAN, Princeton University, Princeton, NJ

RAYMOND L. WHITE, University of Utah, School of Medicine, Salt Lake City, UT

NATIONAL RESEARCH COUNCIL STAFF
WARREN R. MUIR, Executive Director

vi


BOARD ON RADIATION EFFECTS RESEARCH

R.J. MICHAEL FRY (Chair), Oak Ridge National Laboratory, Oak Ridge, TN
S. JAMES ADELSTEIN’, Harvard Medical School, Boston, MA

VALERIE BERAL, University of Oxford, United Kingdom

SARAH S, DONALDSON', Stanford University Medical Center, Stanford, CA
EDWARD R. EPP, Professor Emeritus, Harvard University, Boston, MA
HELEN H. EVANS, Case Western Reserve University, Cleveland, OH
WILLIAM F. MORGAN, The University of Maryland, Baltimore, MD
FRANKLYN G. PRENDERGAST, Mayo Clinic Cancer Center, Rochester, MN.
DANIEL O. STRAM, University of Southern California, Los Angeles, CA

NATIONAL RESEARCH COUNCIL STAFF

EVAN B. DOUPLE, Director, Board on Radiation Effects Research
RICK JOSTES, Senior Program Officer

ISAF AL-NABULSI, Program Officer

CATHERINE S. BERKLEY, Administrative Associate
BRIDGET R. EDMONDS, Project Assistant

BENJAMIN HAMLIN, Project Assistant (effective 10/00)
DORIS E. TAYLOR, Staff Assistant ,

tMember of IOM


Uncertainty regarding the lung-cancer risk posed the by interaction of smoking and radiation
was modeled in the same manner as in the 1985 radioepidemiological tables report, but the
population distribution of smoking habits was updated to reflect a 1993 CDC national survey.
Because of several published analyses of the form of the interaction, the working group chose a
distribution of uncertainties that weighted the result toward a multiplicative interaction. The data

used and the choice of an uncertainty distribution are reasonable.

The working group decided not to try to account for uncertainties in genetic susceptibility to
radiation carcinogenesis. That decision was based on the most comprehensive and authoritative
monograph to date (ICRP, 1998), which indicated that present knowledge does not indicate that
interactions between radiation exposure and genetic sensitivity will have a substantial impact on
population risk estimates. The subcommittee notes that even if that is true at the population
level, it might not be true at the level of the individuals for whom claims are being adjudicated,
inasmuch as at least a few genetic conditions are known to be associated with an increase in
radiosensitivity and more conditions might be identified. The working group should consider

whether to build a special provision into its model to allow for such conditions.

In summary, the subcommittee believes that the working group has for the most part done
an excellent job of selecting data sources on which to base its model of AS. However, there isa
need to clarify its sources of information on uncertainties with, regard to RBE, DDREF, and the
“miscellaneous” category. More thought should also be given to the inclusion of a systematic
risk factor in the model for people with known predisposition to genetic radiosensitivity and the

uncertainties inherent in such a factor.


ACKNOWLEDGEMENTS

This report has been reviewed in draft form by individuals chosen for their diverse
perspectives and technical expertise, in accordance with procedures approved by the National
Research Council’s Report Review Committee. The purpose of this independent review is to
provide candid and critical comments that will assist the institution in making the published
report as sound as possible and to ensure that the report meets institutional standards for
objectivity, evidence, and responsiveness to the study charge. The review comments and draft
manuscript remain confidential to protect the integrity of the deliberative process. We wish to
thank the following individuals for their participation in the review of this report:

John D. Boice, International Epidemiology Institute, Rockville, MD
Shirley A. Fry, Oak Ridge, TN

Donald E. Jose, West Chester, PA :
Kenneth J. Kopecky, Fred Hutchinson Cancer Research Center, Seattle, W.
Stephen Lagakos, Harvard University, Boston, MA

Although the reviewers listed above have provided many constructive comments and
suggestions, they were not asked to endorse the conclusions or recommendations nor did they
see the final draft of the report before its release. The review of this report was overseen by
David G. Hoel, appointed by CLS, who was responsible for making certain that an independent
examination of this report was carried out in accordance with institutional procedures and that all
review comments were carefully considered. Responsibility for the final content of this report
rests entirely with the authoring subcommittee and the institution.

viii


SUSAN E. LEDERER, Ph.D., teaches in the Section of the History of Medicine at the Yale
University School of Medicine and in the Department of History at Yale University. She
received her doctorate in the history of science from the University of Wisconsin, Madison.
Before going to Yale, she taught for a number of years in the Department of Humanities at the
Pennsylvania State University College of Medicine in Hershey, Pennsylvania. A historian of
American medicine, she served as a member of the President's Advisory Committee on Human
Radiation Experiments in 1994-1995. The author of Subjected to Science: Human
Experimentation In America before the Second World War, she has written extensively on issues
related to human and animal experimentation. She is currently writing a social and cultural
history of blood transfusion and organ transplantation in twentieth-century America,

Roy E. SHore, Ph.D., Dr.P.H., is a Professor of Environmental Medicine and Director of the
Epidemiology Program at New York University Medical School. Dr. Shore received his Ph.D.
degree from Syracuse University in 1967 and his Doctorate in Public Health from Columbia
University in 1982. His research interests include environmental and occupational
epidemiology, radiation epidemiology, and epidemiologic methods. He is on the standing
committees on radiation biology/risk assessment of both the International Commission on
Radiological Protection and the National Council on Radiation Protection and Measurements.
He has served on several scientific advisory groups for the National Cancer Institute, Department
of Energy and the Environmental Protection Agency, and on editorial advisory boards of the
Journal of the National Cancer Institute, and Cancer Epidemiology, Biomarkers and Prevention.

DANIEL O. STRAM, Ph.D., is an Associate Professor in the Department of Preventive Medicine
at the University of Southem California. Dr. Stram earned his Ph.D. in Statistics from Temple
University, and engaged in postdoctoral research in Biostatistics at the Harvard School of Public
Health. From 1986-89 he was a member of the Statistics Department of the Radiation Effects
Research Foundation in Japan. Since 1990, Dr. Stram's research interests have focused on
clinical research and epidemiology in childhood and adult cancers at the University of Southern
California and the Children's Cancer Group. His radiation-related work in Hiroshima and U.S.C.
has concentrated on statistical aspects of dosimetry systems used for the A-bomb survivors and
for the U.S. Uranium miner cohort study. Dr. Stram is a member of the Board on Radiation
Effects Research (BRER) of the National Research Council.

DUNCAN C. Tuomas, Ph.D., is Professor of Preventive Medicine, Director of the Biostatistics
Division, and Verna R. Richter Chair in Cancer Research at the University of Southern
California School of Medicine. His primary research interest has been in the development of
statistical methods for cancer epidemiology, but he also has wide ranging interests in both
environmental and genetic epidemiology. On the environmental side, he has been particularly
active in radiation carcinogenesis, having collaborated on studies of cancer in residents
downwind of the Nevada Test Site, uranium miners, medical irradiation, and the atomic bomb
survivors. He was a member of President Clinton's Advisory Committee on Human Radiation
Experiments, as-well as the National Research Council’s Committee on the Biological Effects of
Ionizing Radiation (BEIR V), and radiation advisory committees for numerous other

74


6. APPLICABILITY OF ASSIGNED SHARE CALCULATIONS TO MINORITY
GROUPS

The working group’s draft report gives little consideration to the appropriateness of AS
calculations to minority groups, nor does it provide an alternative for cases in which the baseline
rates of particular cancers differ appreciably between specific minority groups and the average
population. Although data about ethnic differences in population rates may be available, there
are generally few or no data on differences in their radiation risks. In principle, the problem is
analogous to the transport of ERR from the Japanese to the US population. US ethnic groups
clearly differ in some cancer rates; for example, Hispanics have lower rates of breast cancer,
Hispanics and African-Americans have lower rates of thyroid cancer, and African-Americans
have higher rates of prostatic cancer. Because of uncertainty as to whether a multiplicative or
additive model is more fitting for the transport of risk, an “automatic” transport of risk via the
ERR model might be inappropriate. The’ working group should consider what to do about the
transport of risk among ethnic groups; it might have to revise the IREP to allow the transport to

be taken into account.

15


THE NATIONAL ACADEMIES

Advisers to the Nation on Science, Engineering, and Medicine
National Academy of Sciences
National Academy of Engineering
lastitute of Medicine
National Research Council

Executive Director
Commission on Life Sciences

November 21, 2000

Dr. Charles E. Land

Section Chief

Radiation Epidemiology Branch
National Cancer Institute

EPS 7046
6120 Executive Boulevard, MSC 7238

Bethesda, MD 20892

Dear Dr. Land:

On behalf of the Commission on Life Sciences and the Board on Radiation Effects
Research, I am pleased to enclose a “PREPUBLICATION” copy of A Review of the Draft
Report of the NCI-CDC Working Group to Revise the “1985 Radioepidemiology Tables.”
The report is not for public release until Wednesday, November 29, 2000, 10:00am
EST. This report is in response to a request under DHHS contract 200-95-0965, with the
Centers for Disease Control and Prevention, for the National Research Council’s (NRC)
Subcommittee to Review the Radioepidemiology Tables to review the draft report of the
NCI-CDC working group charged with revising the 1985 radioepidemiololgic tables.

Sincerely

In. dL

Warren R. Muir, Ph.D.
Executive Director

Enclosure: 1 copy of report (“PREPUB”)

cc: F. Owen Hoffman, SENES Oak Ridge Inc.

2101 Constitution Avenue, NW, Washington, DC 20418 Telephone (202) 334 2500 Fax (202) 334 1639 national-academies.org


subcommittee. However, we feel obliged to point out some technical anomalies that follow from

the policy.

The subcommittee is disturbed by the implication that the less is known about a potential
association involving low-level risks, the more likely a claimant is to be successful. Consider the
following two scenarios. First, suppose that the relative risk (RR) for a particular exposure is
estimated to be 1.8 with 95% confidence limits (1.7, 1.9), translating to an AS of 44% (CI 41%,
47%). Second, suppose that the RR is estimated at 1.1 with 95% confidence limits (0.22, 5.5),
translating to an AS of 9% (C1 0, 82%), as might happen for a rare tumor type. Clearly, the first
case provides stronger evidence of an association in the population (highly statistically
significant), even though the AS for the group as a whole is less than 50%. In the second case, it
is not clear that there is a causal connection in the population; the association is far from
statistically significant, and the estimated AS is much lower. Nevertheless, under a policy that
provides full compensation to those with upper 99% confidence limits on the AS of at least 50%,
no members of the first group would obtain compensation, and all those of the second group
would be successful. In short, the policy appears to reward lack of information and is

counterproductive.

The subcommittee does believe, however, that uncertainty about age-at-exposure and time-
since-exposure effects, has an important role to play in adjudicating compensation claims. We
believe that the decision about potential causation must be addressed at the population level
before the merits of an individual’s claim are considered. That includes consideration of the
level of statistical significance, the reliability of the epidemiologic database (freedom from bias,
and so on), biologic plausibility and other criteria widely used to assess causality in
epidemiologic associations. In large part, this has already been done by the working group and

the various authoritative bodies on which its estimates are based.

38


context of the mandate. The interactive software offers many advantages over the tables that it

supplements, and there has clearly been careful thought regarding IREP’s implementation.

However, considerable development work will be needed before this product will be
suitable for any but highly specialized audiences. Although the subcommittee endorses the
working group’s approach, in part because of its greater flexibility, we do not believe that a
computer program should replace the printed tables; the latter are potentially more useful to
members of minority groups and economically disadvantaged groups who do not have access to

computers.

The subcommittee concludes that the working group has done an excellent job of selecting
data sources on which to base its model of assigned shares and has made reasonable judgments
in selecting values of the model parameters and characterizing their uncertainties. However,
there is a need to clarify the sources of information on uncertainty with regard to the relative
biological effectiveness of various qualities of radiation, the dose and dose-rate effectiveness
factor, and the miscellaneous other factors that possibly influence risk. More thought should be
given to the inclusion in the model of a systematic risk factor for persons with known genetic
radiosensitivity predispositions. Moreover, the subcommittee was concerned about the problems
with including so many cancer sites for which radiation associations have not been well
established. In such circumstances, subjective confidence intervals can be very wide and could
lead to a situation in which compensation is awarded under dubious conditions of causation
while a scientifically stronger case with a narrow confidence interval fails. The validity of this
approach should be carefully considered. The subcommittee further recommends that the
working group clearly describe and discuss in the report the changes in risk estimates and
uncertainty ranges from the 1985 to the 2000 tables and the likely impact of these changes on

compensation claims.


EXECUTIVE SUMMARY

The National Research Council was asked by the Centers for Disease Control and
Prevention (CDC) to review the draft report of the National Cancer Institute (NCI)-CDC’s
working group charged with revising the 1985 radioepidemiological tables. To this end, a
subcommittee was formed consisting of members of the Council’s Committee on an Assessment
of the Centers for Disease Control and Prevention Radiation Programs and other experts. The
original tables were mandated under Public Law 97-414 (the “Orphan Drug Act”) and were
intended to provide a means of estimating the probability that a person who developed any of a
series of radiation-related cancers, developed the cancer as a result of a specific radiation dose
received before the onset of the cancer. The mandate included a provision for periodic updating
of the tables. The motivation for the current revision reflects the availability of new data,
especially on cancer incidence, and new methods of analysis, and the need for a more thorough
treatment of uncertainty in the estimates than was attempted in the original tables. The

subcommittee discusses this point in more detail in section 10.

The working group has chosen to replace the 1985 tables with an interactive computer
program [Interactive Radio-Epidemiological Program (IREP)]. Their stated aim has been to
provide agencies and individuals with a means of computing estimates of cancer risk after
exposure to radiation that reflect the circumstances of an individual compensation claim better
than was possible with the original tables and of ascertaining the statistical uncertainty inherent
in a particular estimate of radiation-related assigned shares.' The subcommittee believes that this
approach provides a broader coverage of cancer sites of potential interest to individual claimants
than did the 1985 tables, and that the accompanying computer program could have wide
applicability. The working group deserves considerable praise for its efforts, not only with

regard to the specific mandate, but also with regard to a broader understanding of the policy

ee
' The calculated probabilities pertain to populations rather than individuals and as such are not probabilities in the
usual sense, but are properties of the group to which a person belongs. These probabilities are assigned to a person
for the purposes of compensation, and the term assigned share is used to emphasize the difference.

1


¢ The statistical assumptions (and underlying policy decisions) embodied in the tables might

not be clear to new (or even existing) users.

Recommendations

¢ The revised tables should be made available in a variety of forms and distributed through a
variety of mechanisms, including a printed version of the tables analogous to the 1985

document for audiences that would prefer this to computerized delivery.

© A public summary should be developed and made available broadly. It should include a
glossary of terms and concepts for nontechnical audiences, should clearly describe and give
the reasons for the changes from the 1985 tables that are embodied in the revised tables,
should include an explicit table comparing the results of the 1985 and 2000 procedures for

comparable cases, and should describe the process for future revisions plainly.

e The revised tables, in all forms, should include a clear presentation of the statistical
assumptions that are made and the policy implications of the assumptions. The presentation
should be in language and use examples that will be understandable by a lay audience. An
important part of the presentation is to distinguish between PC (AS) and risk. And it must
state clearly that the revised tables cannot be used to estimate risk or to predict the

development of any cancer.

© There should be an intelligible but simple presentation of the details of implementation (what
are the levers that drive the actual results?). The current draft report, although adequate for a

technical audience, will need modification for other constituencies.

Suggestions

© The IREP software needs additional development if it is to be useful to broader audiences. If
broad availability is pursued, the working group should give further consideration to how the

software will be made accessible to the public, including not only mechanisms of

51


(“frailties”) between 80% of individuals (Hougaard et al., 1992). Using this estimate, Thomas
(In press) has described the magnitude of the potential bias and found that the overall correlation
between individuals’ PC that could be calculated if the true model and frailty values were known
and the estimated AS is very high, even if in general the AS tends to underestimate the PC. The
subcommittee is therefore persuaded that the AS approach still has some validity, at least for

relative ranking of claimants.

A number of authors, including the 1984 National Research Council Oversight Committee,
have pointed out various inequities that can arise in a compensation scheme that provides full
payment to claimants with an estimated PC greater than 50% but none to those with PC less than
50%. At the simplest level, one can be disturbed by the profound difference between the
treatments of two individuals with very similar claims. Add to that, the potentially large
uncertainty in the PC estimates and the question arises whether such individual claims can be
distinguished with any reliability. Furthermore, one can envisage situations in which hazardous
exposures were so high that a substantial Proportion of cancers in exposed individuals were
indeed caused by the exposures but none would be entitled to compensation! An employer
might thus avoid paying any compensation claims even though a substantial number of cases
might have been caused by occupational exposure (although regulatory standards and the

ALARA principle would tend to minimize the chances of this).

For such reasons as those, alternative compensation schemes have been proposed, most
based on some kind of sliding scale in which the amount of compensation awarded depends on
the magnitude of the estimated PC or on the uncertainty distribution of the PC estimates. For
example, the 1984 National Research Council Oversight Committee suggested a scheme in
which individuals with PCs greater than 50% would receive full compensation, those with less
than 10% would receive none (to discourage “frivolous” claims), and compensation would be

linearly scaled by PC in the range between 10% and 50%. Such a compensation scheme was

34


governmental agencies. Other environmental activities include studies of asbestos, malathion
spraying in California, electromagnetic fields, and air pollution; he is Co-Director of the
Southem California Environmental Research Center. On the genetic side, Dr. Thomas has
numerous publications in the area of statistical genetics and is collaborating on family studies of
breast, ovarian, colon, prostate and other cancers, insulin dependent diabetes, systemic lupus
erethematosis, and other diseases. He chairs organizing committees for the Genetic Analysis
Workshop and the Informatics Consortium for the NCI Cooperative Family Registries for Breast
and Colorectal Cancer, and is currently President of the International Genetic Epidemiology
Society. These three broad areas of interest make him uniquely qualified to address
methodological challenges in studying gene-environment interactions.

DANIEL WARTENBERG, Ph.D., is a Professor in the Department of Environmental and
Community Medicine at the Robert Wood Johnson Medical School, University of Medicine and
Dentistry of New Jersey. He is also the Chair of the Doctoral Program in the New Jersey
Graduate Program of Public Health. Dr. Wartenberg's main research interest is the development
and application of novel statistical approaches to the study of environmental risk, pollution, and
public health, with particular emphasis on geographic variation and clustering. He has
conducted research on health effects of incinerators, exposure to pesticides and toxic chemicals,
risk assessment methodology, and currently is conducting a study characterizing populations
living near high voltage electric transmission lines.

JOHN S. YOUNG, Ph.D., President of Hampshire Research, is a toxicologist and risk assessor
with extensive experience in toxic chemical policy issues. He has served as the chief scientist for
the research effort that designed the RISK*ASSISTANT software system, and is currently
directing the LifeLine Project to develop software for aggregate and cumulative exposure and
tisk assessment. He directs the community technical assistance program, assisting lay audiences
in the interpretation and use of scientific information. Dr. Young has also been the author of
numerous studies for the U.S. EPA, other state and federal government agencies, the United
Nations and other multilateral organizations. Prior to joining Hampshire, Dr. Young served on
the faculty of the Department of Environmental Health Sciences at the Johns Hopkins School of

Hygiene and Public Health.

75



° Transparency All underlying assumptions must be clearly presented to the
user (and captured in the system’s outputs).

¢ Flexibility If a system incorporates assumptions that are not formalized in
legislative or regulatory policy or that do not represent an overwhelming
scientific or policy consensus, they must be subject to modification by the

user.

According to information provided by the design team, IREP is intended for use primarily
by a comparatively small community of relatively sophisticated users. However, the design team
has given consideration to the system's status as a policy tool and has assumed that it will be
broadly available to the public. The design has attempted to address this assumption. Overall,
the working group has had mixed success in meeting these goals, which might reflect resource

limitations. Specific suggestions for improving the user interface are noted in the appendix to

the working group’s draft report.
Accessibility:

Using IREP requires downloading of a “browser” version of ANALYTICA from a Web site
(http://www.lumina.com). Presumably, if the working group distributes the ANALYTICA-
based version of this software, it will make arrangements to “bundle” the required browser

software with the application.

Development of a Web-based application was discussed by the working group at the first
meeting of the subcommittee. The desirability of such an approach will be dictated by the
expected user base, which in turn reflects the need of the Department of Veterans Affairs (VA)
and other users for this tool to operate openly. In particular, if applicants for benefits will need
access to the program, more work will be necessary to ensure not only access, but also

transparency and user friendliness.

22


With regard to the large number of cancer sites where no statistically significant dose-
response relationship has been identified, the subcommittee notes that inclusion of these tumors
constitutes a large departure from the 1985 tables, which considered only sites with a statistically
significant dose-response relationship. We are sympathetic to the view that most tumors can be
considered to be radiogenic to some degree; in many cases, the low background prevalence of
these tumors in the Japanese population meant that the atomic-bomb study had low power to
detect increased risk. Given that a number of tumor sites are being included in the Working
Group's modeling for which there is not a persuasive association with ionizing radiation, the
subcommittee believes that chronic lymphocytic leukemia (CLL) should be included as well.
CLL could not be studied in the Japanese atomic bomb data because of the rarity of the disease
there. Nevertheless, CLL is more common in Western populations and needs to be considered.
The few data available do not present a clear picture regarding the radiogenicity of CLL, so it
probably should not be ruled out. However we are concerned by the following issue: if the
Japanese data include little information on the ERR of a cancer (because of small background
rates) but the cancer is included, the upper confidence interval of the ERR in an analysis of that
site alone (which could be large) fails to put into proper perspective what we know about that
specific cancer and what we think is true about most tumors (that they are radiogenic to some

degree, but not absolutely).

Reviewing the summary of risk estimates presented in table X of Thompson et al. (1994),
we note that for a wide range of solid tumors there appears to be general homogeneity in dose-
response relationships with a few obvious exceptions—only for digestive cancers (including
stomach cancer) and cancers of the uterus (for which the risk estimate is negative) does the 95%
confidence interval for the individual site fail to overlap the estimate of ERR (0.63/Sv) for all
solid tumors. To formalize the impression of overall homogeneity in ERR, we conducted our
own elementary meta-analysis of the data in table X of that paper, using methods described in
DerSimonian and Laird (1986). Assuming that the 95% confidence intervals presented in table X
have a length of roughly 4 standard errors (which is true asymptotically), we estimated the

39


9. HOW SHOULD THE RADJOEPIDEMIOLOGICAL TABLES BE MADE
AVAILABLE TO THE PUBLIC?

a) Evaluation of the Interactive Radio-Epidemiological Program:

The working group began with the assumption that revision would result in a series of tables
similar in style to the original ones. As work progressed, however, it became apparent that an
alternative existed—to develop an interactive computer program that would make it easier for a
potential user to calculate the risk to an individual, assuming the user has sufficient information
at hand. The working group chose this alternative, and the result is an application for the
personal computer—the Interactive Radio-Epidemiological Program (IREP)—that uses a visual

programming tool, known as ANALYTICA, with an accompanying user’s guide.

The subcommittee notes that consideration of the intended users is basic in any review of a
software system. A system intended for a small, highly trained group would have design
objectives that differ in important ways from those of a system intended for the general public.
Ease of use, for example, is generally far more important for the latter than for the former.
Moreover, systems to support public-policy decisions must meet criteria that do not apply to
other systems, regardless of the audience. In addition to balancing design criteria, such as ease

of use and clarity of presentation, systems that support public-policy decisions must meet the

following three criteria:

e Accessibility To ensure public confidence in the decision process, the system

must be available to all stakeholders affected by decisions that depend on its

use.

21



p. 70-76: It would be useful if these tables gave the 99th percentile estimate from the 1985

calculations for comparison with the new calculations.

58


PREFACE

The National Research Council’s Committee on the Assessment of the Centers for Disease
Control and Prevention’s Radiation Programs was called on to review the draft report of the

The subcommittee members wish to thank the members of the working group who have
contributed to their understanding of the revised tables and to their work. Drs. James M. Smith
and Charles Miller of CDC provided a valuable Perspective on the activities of the Radiation
Studies Branch and useful historical insights for the subcommittee’s study. Drs. Charles Land

their time and thorough in discussing the Strategy adopted in the revision of the
radioepidemiological tables and in demonstrating the interactive computer program developed
for computation of assigned shares, The subcommittee is especially grateful for the information
provided by Dr. Neil Otchin, a representative of the Department of Veteran Affairs,

The subcommittee thanks the National Research Council staff who worked with us, especially

William J. Schull
Chairman

vii


however, estimates for groups of sites might be more reliable, barring convincing evidence that
the sites really are different.

12


in parallel with estimates from comparable scenarios based on the 1985 tables. The analysis

should be included in tabular form in the final report.

* The subcommittee recommends changing the name of the residual “scale factor” to
“additional uncertainty factor” and setting its default value to 1.0 so that it will not affect the
calculations unless a user alters it. If a user wishes to include an additional uncertainty
factor, the program should force the user to name the factor to make clear what variation it
represents and then to specify its GSD. It is to be hoped that the user would justify the
inclusion of the factor and the magnitude of the GSD, so that the factor is not used

capriciously.

¢ The working group should make sure that when, in the “uncertainty setup” window, a user
changes the number of iterations in the simulation, the sampling method, the randomization
method, or the random seed, the program performs the appropriate simulation when any of

the “Calc” buttons are selected. As it is, when these values are changed, none of the “Calc”

buttons functions properly.

It is important that the random seed used in the simulations change with each successive run
of the program unless a user specifies that it remain constant. The program should set as a

default that this value change and should create an option to freeze the value of the random

seed.

© Inthe selection of sample size in a simulation, the default is set to 1,000. That does not yield
particularly stable predictions of the 99th percentile AS value for at least some cancer sites.
We recommend that either it be set higher or, because size is a user option, users be strongly

encouraged to do so in dealing with rare cancers.

Suggestions

¢ To evaluate the importance of cancer sites for which the number of events is relatively rare,
the subcommittee suggested above that future evaluations use either hierarchic random-

effects models or Bayes model averaging to capture the possible dose-related effect at each

49


Information on Committee Members

WILLIAM J. SCHULL, Ph.D., (Chair), is Ashbel Smith Professor Emeritus. His specialty is
human genetics and his primary research interest is radiation biology. In addition to a
distinguished academic career, Dr. Schull previously served as the Head of the Department of
Genetics with the Atomic Bomb Casualty Commission (ABCC) in Japan and later went on to
become one of the Directors of the ABCC’s successor organization, the Radiation Effects
Research Foundation. Dr. Schull is the recipient of numerous awards, including Japan’s Order
of the Sacred Treasure, Third Class. Dr. Schull is a member of several professional societies
including the American Epidemiological Society, the American Society of Human Genetics, the
Radiation Research Society, and the Society for the Study of Human Biology.

SHARON M. FRIEDMAN, MA, is the Iacocca Professor and Director of the Science and
Environmental Writing Program, at Lehigh University. She served as Chairperson of the
Department of Journalism and Communication at Lehigh from 1986-1995. Her research focuses
on how scientific, environmental, technological, and risk issues are communicated to the public.
She served as a consultant to the President’s Commission on the Accident at Three Mile Island
and the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP).
She co-authored the book Reporting on the Environment: A Handbook for Journalists, which has
been translated into 11 languages and widely distributed. She has lectured in many Asian
countries sponsored by ESCAP and other organizations about risk communication and
environmental journalism and served as a Fulbright Distinguished Lecturer in Brazil. Professor
Friedman is the co-editor of Communicating Uncertainty: Media Coverage of New and
Controversial Science and of Scientists and Journalists: Reporting Science as News. She is an
Associate Editor of the journal Risk: Health, Safety & Environment, and a member of the
Editorial Advisory Board of the journal Science Communication. She is a Fellow of the
American Association for the Advancement of Science (AAAS) and a member of the Committee
on Council Affairs and the Council of the AAAS.

PETER G. GROER, Ph.D., is Associate Professor in the Department of Nuclear Engineering at
The University of Tennessee-Knoxville. Dr. Groer earned his Ph.D. in Theoretical Physics from
the University of Vienna, Austria. He teaches undergraduate and graduate courses in Radiation
Protection, Radiation Risk and Reliability Analysis. His research interests include Bayesian
methods for radiation detection, dosimetry, and risk and reliability analysis. He has served on
the editorial board of Risk Analysis. He was a member of the National Research Council’s
(NRC) Committee on the Health Effects of Radon and Other Internally Deposited Alpha
Emitters (BEIR IV) and served on several scientific committees of the National Council on
Radiation Protection and Measurements. He was also a member of the EPA Science Advisory
Board's Uncertainty in Radiogenic Risks Subcommittee and of the NRC Committee on the
Exposure of the American People to I-131 from Nevada Nuclear-Bomb Tests.

73



that study and with the wide range of findings in the experimental literature regarding

neutron RBEs.

pp. 32-33: No indication is given of what the “systematic biases” in the atomic-bomb dose-

p. 34:

p. 37:

p. 37

p. 41:

estimation procedure consist of.

No justification is given as to why 5 hours is the critical value to distinguish acute from

fractionated exposures in the assigned-shares model.

Figure IV.F.3 might be easier to interpret if presented in terms of the dose-response

curves themselves, that is, 1 + BDX DDREFyeute (D).

and elsewhere: The mode of Presentation (especially graphic) of the DDREF is
counterintuitive and might confuse many people who are not conversant with the lingo of
radiobiology. One normally thinks of a “factor” as multiplicative or additive, but in the
case of DDREF it is a division factor. It would be better to plot 1/DDREF (to show that a
higher DDREF yields Jess effect, not more, as the graph seems to imply). Text should be

added on p. 33 to clearly indicate that, for example, a DDREF of 2 implies half as large a

slope.

Arguably, one should attach some uncertainty to the low-LET doses, because gamma
exposures are believed to have a slightly lower RBE than 250-kVp x rays, and low-

energy x rays are thought to have a higher RBE than 250-kVp x rays.

p. 46, section 2: Did Land and others [1994] restrict their analyses to age at first full-term

Pregnancy, or were the other standard breast-cancer risk factors also examined? How

56


Any printed version of a public summary should include well-designed graphics to help to
explain the complicated concepts involved in the IREP. It also should include many of the
graphic pages in the current user's guide that show how the miodel works. However, the language

in the guide, which is for a technical audience, will not work well for the general public.

Finally, but no less important, the subcommittee notes that ethical and communication

issues transcend the use of a Web site or printed material. The paramount ones are these:

© How should the issue of privacy of medical information be addressed?

¢ How will people find out about the revised tables?

29


Greenland S. Second-stage least squares versus penalized quasi-likelihood for fitting
hierarchical models in epidemiological analyses. Stat Med 16:515-26, 1997.

Greenland S. Relation of probability of causation to relative risk and doubling dose: a
methodologic error that has become a social problem. Am J Public Health 89:1 166-1169,
1999.

Greenland S. Epidemiology, justice, and the probability of causation. Jurimetrics, In press.

Groer PG. Probabilistic Causality and Radiogenic Cancers. Proc. Fourth International
Radiopharmaceutical Dosimetry Symposium. Oak Ridge, TN: Oak Ridge Associated
Universities, 1985.

Hougaard P, Harvald B, Holm NV. Measuring the similarities between the lifetimes of adult
Danish twins born between 1881-1930. J Am Statist Assoc 87: 17-24, 1992.

ICRP (International Commission on Radiological Protection). Genetic susceptibility to cancer.
Annals ICRP. 1998, 28 (Publication 79): 1-157.

Karagas MR, Greenberg ER, Spencer SK, Stukel TA, Mott LA. New Hampshire Skin Cancer
Study Group. Increase in incidence rates of basal cell and squamous cell skin cancer in
New Hampshire, USA. Int J Cancer 81:555-559, 1999.

Laplace PS de. Memoire sur la probabilite des causes par les evenemens. Memoires de
Mathematique et de Physique, Presentes a V’Academie Royal des Sciences, par divers
Savans et lus dans ses Assemblees, Tome Sixieme, pp. 621-656. (Reprinted in Laplace’s
Oeuvres Completes, 8, pp. 27-65), 1774.

Lindley D. Making Decisions. Second Edition. New York, NY: John Wiley and Sons, 1985.

69



10. FUTURE REVISIONS AND BROADER ISSUES

Future Revisions:

The present set of analyses of radiation data for deriving AS estimates are based almost
exclusively on the Japanese atomic-bomb data. In the future, it would be valuable to use a
broader range of data (for example, those to be presented in the impending reports from the
United Nations Scientific Committee on the Effects of Atomic Radiation and the Biological
Effects of Ionizing Radiation (BEIR VII, phase 2)), either to analyze conjointly or to use in
evaluating the consistency and uncertainty of the results. In addition to helping to ensure the

validity of the radiation-risk assessment, this would lessen the uncertainties.

The subcommittee further suggests that future analyses explore an alternative approach
based on hierarchic random-effects models (Witte et al., 1994; Greenland 1992, 1993, 1997). In
this approach, site-specific cancers might be grouped a priori in some hierarchic fashion (for
example, according to levels of the International Classification of Diseases). Each branch of the
hierarchy would be ‘associated with a variance component for slopes of dose-response
relationship and perhaps for the magnitude of modifying effects of age, sex, latency, and so on.
These variance components would not be specified in advance, but would be estimated from the
data. The slope estimates (and modifying effects) for any given cancer type would then be
weighted averages of the direct estimates for the specific types and for the aggregate estimates at
the higher level(s) of the hierarchy, each weighted by corresponding variances. Thus, for groups
of cancers showing little difference in their slope estimates or modifiers, the smoothed estimates
would be dominated by the overall model for the larger group, and estimates for cancers for

which there is compelling evidence of substantial variation would be dominated by the subgroup

estimate.

A somewhat related approach known as Bayes model averaging (Madigan and Raftery,

1994; Carlin and Chib, 1995; Carlin and Louis, 1996) might be considered to address questions
30


It is suggested that the working group reevaluate its decision to use time-since-exposure as a
modifier of AS only when there is a linear temporal slope. Several tumor types show

nonlinear temporal trends that might need to be considered.

© The working group should consider how to transport PC calculations to minority populations
in the United States when minority group’s baseline rates differ appreciably from the average
US rates. In principle, this is similar to the problem of transporting risk estimates from the
Japanese to the US population. The subcommittee recognizes that the transport of PC
calculations is compromised by the difficulty of obtaining reliable estimates of causality in

subpopulations on which the data are inadequate.

Epidemiologic and Biostatistical Models

Conclusions

e In general, the biostatistical methods used to analyze the epidemiologic data from the atomic-

bomb survivors are appropriate.

e The working group’s treatment of the large number of tumor sites for which no significant
dose-response pattern has been found in the Japanese data is of concen. These are described
in the working group’s report as constituting almost half the tumor sites considered. The
inclusion of such tumors in the current report is a major departure from the 1985 report, in
which only sites with statistically significant dose-response relations were considered. The
subcommittee supports the view that these tumors ought to be included in some way, but we
think that using separate analyses for each of the tumors has unfortunate practical side
effects, particularly with regard to how the VA has tended to use upper confidence limits as
criteria for compensation. Moreover, the use of methods for deriving an AS in this setting
can lead to the false impression that such calculations have the same scientific rigor as those

associated with tumors that have been shown to be radiogenic. This would have important

45


The working group should provide a summary of its approach pointing out the advantages
and the limitations of the Monte Carlo method. For example, it might note that Monte Carlo
simulations offer greater flexibility in modeling the uncertainty distribution of estimates of a
particular quantity. It might also note that the method requires that each parameter estimate be
assigned a distribution. The distributions can be specified as theoretical functions or they can be
specified empirically. Then, for each run of a large set of simulations—say, 10,000—a parameter
set is developed by selecting a value for each parameter from its specified distribution. The
sampling strategy can be random or structured so as to emphasize particular parts of the
parameter hyperspace (for example, by Latin hypercube sampling). Once selected, the parameter
values are combined by using a previously specified equation into an overall estimate of the
probability of causation. Two advantages of this method are that the user can specify any
distribution shape for each uncertainty distribution and that the combination of uncertainties is
not limited to additive and log-additive (ERR) models. Two limitations are that one has to
estimate the shape and variance of the uncertainty distribution for each parameter and that one
has to run enough simulations to be confident in the stability of the estimates, particularly in the
tails of the distribution. If all the distributions needed for a Monte Carlo stimulation are
specified as lognormal and they are combined in a multiplicative model (the sum of the
logarithms), the results should be essentially identical with the results of the old method. It is
important to note that in both instances the calculations include only the uncertainty distributions
specifically noted. As stated in both the 1985 report and 2000 draft report, many other aspects of

uncertainty are not included and are likely to give rise to additional uncertainty.

Both the method used by the working group and the one suggested above assume that all
parameter uncertainty distributions are independent (uncorrelated). If parameter estimates were
positively correlated, failure to model their dependence would result in underestimates of the
uncertainity. One difference between this approach and that used by the EPA, which it-would be
useful to point out, is how the slope-factor uncertainty is modeled. EPA uses the 95% upper

confidence limit of the potency-slope factor as a fixed-point estimate; IREP uses a regression-

18


implementation. The analyses for some of those sites were based on relatively few events,
Therefore, even if point estimates were large enough to suggest an important effect, the
confidence intervals were so broad because of sample size that the effect was not significant.
A result that is not statistically significant can be interpreted inappropriately as evidence of

no effect.

The subcommittee observed some processing problems when the “uncertainty setup” window
was accessed and options changed. It is important for the user to be able to choose
alternative options in this window and have the program still perform the appropriate
calculations. It is also important that the random seed used for the simulation program
change with each run unless a user specifies that it be constant for a series of runs. Currently,
the values in this table are not recalculated unless a user changes the inputs. Clicking the
button after closing the window has the effect only of reopening the window. The same is
true of the “preceding results” button that shows the inputs and the ERR. To recalculate,
users have to change the inputs. That means that someone who runs a case will always get
the same answer until another case is run; returning to the first case will now yield identical
answers. It also seems; however, that the seed is reset whenever ANALYTICA starts, so by

Tunning the same sequence of cases one would generate consistent answers.

Recommendations

The assumptions and methods implicit in the use of Monte Carlo simulation methods (such
as the independence of each of the parameters) and their implications relative to the 1985

methods should be stated clearly.

To elucidate the differences in the estimated AS values in the current working group’s draft
report from the 1985 report, the subcommittee recommends that a more extensive
comparative analysis of the IREP program output be conducted than has been done for the
current draft report. It could be implemented as a factorial evaluation over a reasonable

sample of values for each of the parameters, and the resulting AS values could be presented

48


p. 47,

was this variable coded for nulliparous women? Even though these authors adopted the
parameterization given here, elsewhere in this report 6 = 1 corresponds to the
multiplicative model and 6 = 0 to the additive model. Perhaps internal consistency would

be less confusing.

last paragraph: The three references to tables IV.G should be to tables IV.I. Some
discussion of the literature on alternative forms of mixture models (such as,

multiplicative, Guerro-Johnson, and Box-Cox models) might be worth while.

p. 49, section J: The possibility of an interaction of the ATM gene with ionizing radiation should

p. 49:

be mentioned here. Although experts differ about the ATM-radiation interaction, it
would be right to raise the concerns that there is some controversy about it (Wong et al,

1997; Nichols et al, 1999; Shafiman et al, 2000).

It is doubtful that xeroderma pigmentosa patients have increased susceptibility to
carcinogenesis induced by ionizing radiation, even though they are exquisitely sensitive
to ultraviolet radiation. Did the authors instead mean nevoid basal-cell carcinoma

syndrome patients who have a well-documented radiosensitivity?

p. 51, section 2: Why were these two situations excluded?

p. 53,

section 5b: It seems inconsistent with the treatment of other uncertainties to fix the

coefficient of D’ at 1.0 as though it is known with certainty.

p. 54, section 7: The reference to section IV.G appears incorrect, was IV.D intended?

57


PE NATIONAL ACADEMIES

National Academy of Sciences
National Academy of Engineering
Institute of Medicine

National Research Council

The National Academy of Sciences is a private, nonprofit, self-perpetuating society of distinguished
scholars engaged in scientific and engineering research, dedicated to the furtherance of science and
technology and to their use for the general welfare. Upon the authority of the charter granted to it by the
Congress in 1863, the Academy has a mandate that requires it to advise the federal government on
scientific and technical matters. Dr. Bruce M. Alberts is president of the National Academy of Sciences.

The National Academy of Engineering was established in 1964, under the charter of the National
Academy of Sciences, as a parallel organization of outstanding engineers. It is autonomous in its
administration and in the selection of its members, sharing with the National Academy of Sciences the
responsibility for advising the federal government. The National Academy of Engineering also sponsors
engineering programs aimed at meeting national needs, encourages education and research, and
recognizes the superior achievements of engineers. Dr. William A. Wulf is president of the National
Academy of Engineering.

The Institute of Medicine was established in 1970 by the National Academy of Sciences to secure the
services of eminent members of appropriate professions in the examination of policy matters pertaining to
the health of the public. The Institute acts under the tesponsibility given to the National Academy of
Sciences by its congressional charter to be an adviser to the federal government and, upon its own
initiative, to identify issues of medical care, research, and education. Dr. Kenneth I. Shine is president of

the Institute of Medicine.

The National Research Council was organized by the National Academy of Sciences in 1916 to
associate the broad community of science and technology with the Academy’s purposes of furthering
knowledge and advising the federal government. Functioning in accordance with general policies
determined by the Academy, the Council has become the principal operating agency of both the National
Academy of Sciences and the National Academy of Engineering in providing services to the government,
the public, and the scientific and engineering communities. The Council is administered jointly by both
Academies and the Institute of Medicine. Dr. Bruce M. Alberts and Dr. William A. Wulf are chairman
and vice chairman, respectively, of the National Research Council.

iii


included in this latter category so that readers would have a clearer basis for reaching their own
decision about how large an allowance should be made for unquantifiable uncertainty. It is
impossible to anticipate all possible sources of uncertainty, but they include differences between
populations (for example, the US and Japanese populations) in the ERR coefficients that are
being transported, misspecification of the form of fitted models, and uncontrolled confounding
and other biases in epidemiologic studies. It should also be clarified that this category is
intended to reflect uncertainty only in the estimated AS for the US population as a whole, not for
individuals, in that the range of individual variation could be arbitrarily large and there is no

basis for estimating just how large it could be.

As to compensation, some agencies, such as the VA, recognizing uncertainty, have used an
upper confidence limit on AS (the 99th percentile in the VA) as a screening criterion to weed
out claims that have little chance of being successful. In practice, however, few claims that
satisfy the VA’s screening criterion appear to have been eliminated in the adjudication process,
presumably because little additional information can be brought to bear on the question of
causality that was not already taken into account in the AS calculation—at least not information
that offers a quantitative basis for shifting the estimate substantially. Hence, the upper 99%

percentile is effectively the de facto compensation criterion.

That policy has the merit of liberality in that few claimants whose true PC (if it can be
estimated) is greater than 50% will be denied compensation because their estimated PC is less
than that value as a result of these types of uncertainty. However, this needs to be weighed
against the expected number of payments to individuals with low “true PCs”, which would be
provided at the expense of all taxpayers or at the expense of other groups that are more deserving
of government benefits. It is possible that the loss to such other groups is individually minuscule
in comparison with the benefit to those who merit compensation but might be denied it under a

more restrictive policy. Thus, this is a policy question that goes beyond the charge to the

37


implemented by British Nuclear Fuels Ltd and the UK Atomic Energy Agency even before the
1984 NIH report (Thomas and others, 1991).

Robins and Greenland (1991) have argued that compensation schemes that pay in proportion
to PC are neither “robust” to model misspecification nor “economically rational”. In this
context, a payment scheme is “robust” if the total amount paid to individuals actually harmed by
exposure is the same under the two models, and “economically rational” if the total amount paid
to all workers is the same. Those terms are intended to imply that the total amounts paid to
individuals under an erroneously specified model will be equal to the total amounts that would
have been paid under the correct model even if the payments to specific individuals were

inequitably distributed under the erroneous model.

However, the same authors have considered other compensation schemes and contend that
one based on the estimated loss of life expectancy (LLE) has some theoretical advantages. If
payment is proportional to the estimated average loss of life, which can be estimated without
invoking untestable assumptions, such a scheme is both robust and economically rational in the
sense defined above and also provides a rationale for a gradient in compensation based upon the

age of onset of the cancer (Robins and Greenland, 1991).

The choice of PC as the basis of compensation policy has specific implications. For
example, suppose two populations have the same absolute excess risk of cancer due to radiation
exposure but have different base line risks. In the high-baseline-risk population, the PC will be
lower because it is more likely that the cancer was caused by factors other than radiation.
However, the expected LLE due to radiation exposure in the total population (cases and
survivors combined) will be about the same in the two populations if the disease is rare. In the
latter case, the LLE is roughly proportional to the product of life expectancy in the general
population and excess absolute risk, which we are assuming is the same in the two populations.

Thus, the expected LLE due to radiation among cases would be higher in the low-risk population

35


Installation

Given that the installation appears to use Install Shield, it should be possible to install the
IREP files and the ANALYTICA browser module in a single operation. That would make the

system a little easier for computer novices,

The version of ANALYTICA that is used is a trial version. Is there a simple browser

version that can be installed without requiring the user to face the registration screen?

The setup program runs fine off the CD. Why is the user instructed to copy it to the hard

drive?

ANALYTICA Constraints
It is mildly annoying that ANALYTICA does not appear to support the wheel mouse.

Some buttons appear to work on a single click; others require a double click. It is not

intuitive in advance which are which. Double-clicking on a single-click button drops the user

into an empty (blank) window.

The redrawing of windows has some problems. In addition, ANALYTICA sometimes

opens windows in odd locations; some have to be dragged back to the screen center for closing.

The display of scroll bars is somewhat insensitive to completeness of display: they imply

that a window contains more than is shown, whereas all window content is visible.

Many more objects are selectable than is desirable in a system that will support
inexperienced users. For example, clicking practically anywhere in the screen calls up a
definition. Ideally, one wants to control all the selections that a user can make. Similarly, many
of the menu-bar and tool-bar choices are of little interest to the user, as opposed to the system

designer. Can they be Suppressed or activated selectively?

60


2, SUMMARY OF THE SUBCOMMITTEE’S REPORT

The revised radioepidemiological tables provide an update of the probabilities of causation
based on new epidemiologic data and statistical models, and they represent a comprehensive
attempt to model the uncertainty in estimating radiation risks. The working group has used a
systematic approach to analyze radiation effects identified in studies of the Japanese survivors of
atomic bombs; so risk estimates for the various tumor sites should be comparable. It has also
provided broad coverage of tumor sites, so its draft report and the accompanying computer

program are widely applicable.

The working group used information on uncertainty in its models when it was available and
made sensible choices of ranges of uncertainty when direct information was not obtainable. It
then applied a Monte Carlo analysis to model the uncertainty inherent in the risk estimates.
Their Monte Carlo approach allowed flexibility in the modeling of uncertainties, rather than

constraining the model to fit specific distributions.

The provision of a computer program incorporating the risk and uncertainty models allows
users to model the circumstances of their exposure and to derive an overall estimate of radiation-

associated AS. For all those steps, the working group is to be commended.


© Further work on modeling the likely bias in AS due to genetic or environmental differences
in an individual’s cancer susceptibility (on the basis of whatever data exist on familial
aggregation of cancer diagnoses, for example) might ultimately serve as a useful adjunct to
the working group’s report. However, we do not recommend that corrections for such biases
be included in its current report, given the difficulties in assessing the variability of

individual baseline rates.

Handling of Uncertainty

Conclusions

e The working group propagated the uncertainty of the estimate of ERR and associated AS by
applying Monte Carlo methods to the estimated distribution of each of the parameters rather
than combining lognormal distributions around point estimates for those parameters, as was
done in the 1985 radioepidemiological tables. That has the advantage of allowing more
flexibility in describing the uncertainty distribution around each parameter and in combining
uncertainty distributions, although the assumptions on which it is based differ from those of
the previous method. The subcommittee supports this approach but notes that the

assumptions and methods of the approach must be stated clearly.

e The subcommittee was uncomfortable with the formulation of the residual “scale factor”.
Although we recognize the intention of capturing unspecified sources of bias and uncertainty
about which we have no useful information, we believe that it is inappropriate to specify a
distribution for unspecified factors. We do see the utility of including an option for users to

specify an additional uncertainty factor if an appropriate justification is provided.

* The working group chose to evaluate AS for several anatomic sites for which there is no
statistically significant dose-response pattern, in contrast with the approach used for the 1985

report. The subcommittee supports this in concept but is concerned about its

47


42. SUGGESTIONS FOR FUTURE COMPENSATION POLICY

Current compensation policy is based on the tort-law principle of preponderance of
evidence, which has been widely interpreted, as requiring that a claimant’s estimated probability
of causation be at least 50%. The subcommittee has some misgivings about this approach to
settling claims. It makes no explicit provision, for example, in the uncertainty inherent in the PC
estimate itself, However, a reasoned approach to compensation for cancers possibly caused by
prior exposure to ionizing radiation should follow the rules for rational decision-making under
uncertainty. Those rules demand that uncertainties of possible outcomes be specified and that
the various consequences of actions be described in terms of utilities (Lindley, 1985). Such an

approach to compensation is missing and reeds to be developed. The subcommittee notes, too,

that

© The all-or-none threshold can lead to substantial differences in the treatment of individuals
with minimal differences in their true PCs. Here the subcommittee is not suggesting the 50%
probability as a “bright line” cutoff, but rather the award of compensation on a sliding scale
depending on the posterior probability that the PC exceeds 50%, on the bases of its

uncertainty distribution.

© Theoretical arguments have demonstrated that unobservable heterogeneity between
individuals can lead to a downward bias in the epidemiologically derived AS as an estimator
of the average of individuals’ PCs and that the magnitude of this bias will vary across

individuals in a way that depends on such factors as age, dose, and unobservable baseline
risk.

© PCs cannot be estimated with certainty, but the distribution of their possible values can be
derived on the basis of reasonable assumptions about the remaining components of

uncertainty, such as those implemented in the current IREP program, and such uncertainty

needs to be taken into account in resolving claims.

33


of these exclusions, except to note that the present report is only "an interim update” in light of
the Biological Effects of Ionizing Radiation (BEIR VII, phase 2) report expected in a few years,
which will provide a much more thorough analysis of radiation risks but not for the two cancer
risks exluded. Certainly, the incorporation of radon exposures would require special treatment
based on completely different data sources from the atomic-bomb survivor cohort, namely, the
extensive reanalysis of 11 cohorts of underground miners and case-control studies of residential
radon performed by the BEIR VI Committee (NRC, 1999). Although, it can be anticipated that
the addition of a module for computing ASs for radon-exposed lung cancers will be part of the
next update of IREP, it is unfortunate that it was not included in the present report, inasmuch as
there is a particularly timely need for it. The BEIR VI report provides a well-established model
for computing ASs and their uncertainties without having to undertake any new data analyses. In
response to the recommendation by the President's Advisory Committee on Human Radiation
Experiments (ACHRE) that compensation policy for the Colorado Plateau uranium miners be re-
examined, the Department of Justice (DOJ) convened an expert panel in 1996 to prepare a new
set of radioepidemiological tables (DOJ, 1996) for use in developing an amendment to the
Radiation Exposure Compensation Act (RECA). As noted by ACHRE, the scientific basis of the
RECA has advanced considerably since the act was passed, and a more appropriate basis for

compensation is badly needed.

Data Sources for Estimates of Uncertainties

The working group based its evaluation of uncertainty in radiation risk coefficients and
models primarily on analyses of data that have been conducted with the studies of the Japanese
survivors of atomic bombs. For specific cancer types the working group evaluated uncertainty
distributions by sex, age-at-exposure, time-after-exposure, and age-at- -observation. For thyroid
cancer, the working group’s uncertainty estimates were based on an analysis of pooled data from

several studies (Ron et al., 1995).


NATIONAL ACADEMY PRESS * 2101 CONSTITUTION AVENUE, NW * WASHINGTON, DC 20418

NOTICE: The project that is the subject of this report was approved by the Governing Board of
the National Research Council, whose members are drawn from the councils of the National
Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. The
members of the committee responsible for the report were chosen for their special competences

and with regard to appropriate balance.

This report was prepared under the Centers for Disease Control and Prevention contract 200-95-
0965 between the National Academy of Sciences and the Centers for Disease Control and
Prevention.

International Standard Book Number 0-309-07250-6

A limited number of copies of this report are available from the:
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2101 Constitution Avenue, NW

Washington, DC 20418

(202) 334-2232

Copyright 2000 by the National Academy of Sciences. All rights reserved.
Printed in the United States of America


A Web site that includes technical information and models will not automatically be an
effective communication tool. Careful thought and planning will be essential in the design and
monitoring of the use of the Web site to ensure its effectiveness in disseminating information

about the revised tables. Questions to consider about the Web site include these:

¢ Will the Web site include means to measure its effectiveness and the
applicability of the model developed by the working group?

¢ Will measures of the effectiveness of the Web site and the model be built into
the site?

¢ How user-friendly can this program be with its complicated information?

¢ Should there be other parts to the Web site, including a chat room or a
frequently-asked-questions section?

¢ Will there be a way for a user to get e-mail information from a VA
representative about problems in using the site or about interpreting the
information in the model regarding his or her case? Would such a system add
to the present workload of the VA?

¢ Who will maintain and update the Web site as needed?

¢ Consideration should also be given to possible Web sites in languages other

than English, notably Spanish.

In addition to the Web site, however, the subcommittee recommends that a printed public
summary be available to potential users and family members who do not have ready access to
computers and the Internet. The question of computer accessibility also comes up for members
of lower economic and minority groups. To get information to those groups, it will be important
to have a printed public summary-perhaps in brochure or newsletter format—that is widely

distributed to ensure that everyone interested in VA compensation issues gets a copy.

28


Madigan DM, Raftery AE. Model selection and accounting for model uncertainty in graphical
models using Occam's window. J Am Statist Assoc 89:1535-46, 1994,

Miller DL, Weinstock MA. Nonmelanoma skin cancer in the United States: incidence. J Am
Acad Dermatol 30:774-778, 1994,

NCRP (National Council on Radiation Protection and Measurements). Uncertainties in Fatal
Cancer Risk Estimates Used in Radiation Protection. National Council on Radiation
Protection and Measurements. NCRP Report No. 126. Bethesda, MD, 1997.

NIH (National Institutes of Health). Rall JE, Beebe GW, Hoel DG, Jablon S, Land CE, Nygaard
OF, Upton AC, Yalow RS, Zeve VH. Report of the National Institutes of Health Ad Hoc
Working Group to Develop Radioepidemiology Tables. National Institute of Health,
Bethesda Maryland, 355 pp., 1985,

Nichols KE, Levitz S, Shannon KE, Wahrer DC, Bell Dw, Chang G, Hegde S, Neuberg D,
Shafman T, Tarbell NJ, Mauch P, Ishioka C, Haber DA, Diller L. Heterozygous
germline ATM mutations do not contribute to radiation-associated malignancies after
Hodgkin’s disease. J Clin Oncol 17:1259, 1999.

NRC (National Research Council) Oversight Committee on Radioepidemiological Tables.
Assigned Share for Radiation as a Cause of Cancer. Washington, DC: National Academy
Press, 1984.

NRC (National Research Council) Committee on the Biological Effects of Tonizing Radiations.

Health Effects of Exposure to Radon: BEIR VI. Washington, DC: National Academy

Press, 1999,”

70


7. ESTIMATION OF ASSIGNED SHARE FOR NONMELANOMA SKIN CANCER

The subcommittee recommends that the working group develop AS estimates for
nonmelanoma skin cancer (NMSC), because NMSC commonly requires adjudication by
compensation boards and there is clear evidence that radiation can cause skin cancer. It should
be possible to develop a pooled risk estimate from the several major studies of radiation and
NMSC and to estimate from them the importance of appropriate features or risk modifiers, such
as age-at-exposure, race, and shape of the dose-response curve (Ron et al., 1991; 1998; Shore et
al., 1984). If so, the working group should take into account the likelihood that the dose-
response relation is nonlinear with little evidence of an effect in the low-dose range of interest in

these radioepidemiological tables.

Although baseline rates of NMSC in the United States are not well characterized for recent
years, it should be possible to estimate the US rates in Caucasians on the basis of earlier rates
obtained by Scotto and others (Scotto, 1974, 1983) in the SEER Registry study, with adjustment
to reflect temporal trends in NMSC rates as reported by them and others (Fears and Scotto, 1982;
Glass and Hoover, 1989; Karagas et al., 1999; Miller and Weinstock, 1994). The added

uncertainty associated with the estimated US baseline rates might be incorporated as part of the

transport uncertainty.

16


ADVANCE COPY

NoT FOR PuBLic RELEASE BEFORE

Wednesday, November 29, 2000
10:00 a.m. EST

PLEASE CITE AS A REPORT OF THE
NATIONAL RESEARCH COUNCIL

A Review of the Draft Report of the

NCI-CDC Working Group to Revise

the "1985 Radioepidemiological Tables"



standard errors of the risk for the various sites. Removing some obvious overlap in table X
(between stomach cancer, colon cancer, and digestive cancers as a whole), we arrived at the ERR

estimates and standard errors in table 1, appendix C.

A formal test for homogeneity of the relative-risk estimates in table 1 using the methods
described in DerSimonian and Laird (1986) strongly rejects the hypothesis of homogeneity (chi-
square = 78, 18 df, p<0.0001), but that is due principally to two cancer sites (cancers of the
breast and of the uterus). Removing those and two other sites known to be strongly hormone-
related (thyroid and ovary) reduces the evidence for nonhomogeneity of the ERR estimates in
table 1 to marginal significance (chi-square = 22.6, 14 df, p = 0.067). The fact that there is
relatively little evidence of heterogeneity for the remaining cancers suggests that information
about them may indeed be pooled to describe the uncertainty in our estimate of ERR for any
individual site. A formal way of doing this is also described in DerSimonian and Laird: the
fitting of a random-effects model (a type of hierarchic analysis) and the calculation of posterior
estimates of each site’s ERR and confidence intervals. That is done in table 2, appendix C for

the 15 non-hormone-related cancers.

Columns 2-4 reproduce the ERR estimates and lower and upper confidence intervals from
table X of Thompson et al. (1994). Columns 5-7 give the predictions and 95% confidence
intervals from the random-effects analysis; these were obtained with the hierarchic modeling
program, MLn (appendix C gives the MLn program log). Note that the estimates from the
random-effects analysis are all pulled toward a weighted average of the ERR estimates for the 15
sites (the weighted average was 0.44/Sv). And note especially the results for rare sites, such as
kidney. The upper confidence interval of the ERR for this site was 2.2 ERR/Sv in Thompson’s
analysis, but the lower confidence interval was less than zero. In the random-effects analysis the
breadth of the confidence interval is reduced by a factor of 3.6, with the upper limit now equal to

0.82 ERR/Sv. Such an uncertainty estimate appears to the members of the subcommittee to be in

40


less-experienced users will need carefully prepared explanatory material that is accessible
directly within the IREP.

The system presents its report on the contribution of various factors in extrapolation to
uncertainty only in relative terms, although it calculates the absolute magnitude of the
uncertainty. It might be helpful to offer users a choice of how to present information on each
component’s contribution to total uncertainty in the prediction of AS, that is, to supplement the

pie-chart analysis with a stacked-bar presentation of the data.
Flexibility:

A variety of options are available to permit personalization of the computations, including
personal information (age, sex, and the like), exposure (number, years, organ dose, dose rate, and
exposure type), and type of cancer. However, because IREP’s uncertainty-analysis modules are
the driving feature of the decisions made by this system, it is important for users to know that the
assumptions made in assessing uncertainty fall into three categories: locked by policy (and
whose policy has locked it), current state of the science (and what peers have vouched for it), and

modifiable by the user.

The last category seems to be relatively rare (particularly if the distributed form of the
software will be analogous in operation to ANALYTICA in the browser mode). The
development team has clearly gone to great lengths to make underlying assumptions accessible
to users (including the mathematical description of how assumptions are operationalized), but
these choices cannot be user-modified. The inability of users to modify critical system

assumptions calls for greater attention to documenting the policy and scientific foundations of

the assumptions.

24


presence of some kinds of individual variability, a person’s PC cannot be estimated without
recourse to additional assumptions that are inherently unverifiable. They describe examples of
hypothetical models in which every case’s date of death was advanced by exposure (perhaps only
by a small amount), although the ERR (and the PC) could be arbitrarily small. They interpret
that situation as implying that exposure “contributed to” disease in 100% of the cases and hence
that some compensation would be deserved, but none would qualify under a PC (or AS) criterion
that required at least a 50% probability. However, if a set of individuals, exchangeable with this
particular individual, can be identified, the parameters of a suitable dose-response model and the
PC can be estimated under the assumptions specified by Robins and Greenland (1989). It should
be noted, nonetheless, that parameter estimation in the presence of competing risks usually
assumes independence of the event of interest and the competing risks. If that assumption is
dropped, the estimates of the model parameters and of the PC will change. The form of the
dependence is not known, so one can only establish bounds for the parameter estimates. These

bounds correspond to extreme forms of dependence.

Robins and Greenland (1989) show further that the AS computed for a group of individuals
will not, in general, be a valid estimator even of the mean of the individuals’ true PC and will
usually be an underestimate of the true mean PC. Again, unverifiable assumptions would have
to be invoked to allow such an interpretation of the AS as a mean of individual PCs; such
assumptions would include the absence of individual variation in baseline risks, an assumption

that is almost certainly false.

The subcommittee agrees with the Robins and Greenland’s mathematical analysis but is
somewhat skeptical about its practical significance. The magnitude of the bias in the AS
depends on the extent of the population heterogeneity in baseline rates. Although this
heterogeneity cannot be estimated from incidence or mortality data on independent individuals,
family or repeated-events data can provide an idea of how large this variability could be. Data

on Danish identical twins lead to an estimate of about a 7-fold range in multiplicative factors

33


based approach to estimate the parameter and its uncertainty distribution. Another difference is
that EPA sometimes uses two-dimensional Monte Carlo analysis, which separates effects of
variation from those of uncertainty. IREP does not make that distinction. Clarifying those
differences and explaining their consequences would be informative for users. Although the
specification and use of GSDs to describe the uncertainty of specific parameters and the
combination of these values algebraically are fairly transparent, the use of IREP is opaque. For
the general public, a clear discussion of how it works, with limitations and assumptions, would

be useful. It might also be worth while to point out the slope-factor uncertainty.

Finally, the working group conducted inadequate sensitivity analyses to show some of the
effects of the changes in the data and methods quantitatively. Some of the AS values estimated
with the IREP will be lower than those listed in the older tables, so it will be important to explain
clearly which changes in data or modeling led to the lower estimates. In short, the impact of

each change in how AS has been modeled must be described, including the uncertainty

modeling.

Residual Uncertainties and Use of a Scale Factor

Following the method set forth in NCRP Report 126 (NCRP, 1997), the working group has
included a “catch-all” factor for additional, nonspecific sources of uncertainty. Its report notes
that a precedent for this was set in the 1985 tables. The additional, nonspecific sources are noted
in the text, but the subcommittee does not see that they were included in any of the calculations

(except for smoking) and this should be made clear.

The subcommittee is uncomfortable with the inclusion of an uncertainty distribution for
unknown or unidentified factors and with the rationale for it. On the one hand, it can be argued

that the use of the 99th percentile limit in the AS in effect already allows for this and that an

19


1. INTRODUCTION

On April 18, 2000, the National Research Council was asked by the Centers for Disease
Control and Prevention (CDC) to review the draft report of the National Cancer Institute (NCI)-
CDC’s working group charged with revising the 1985 radioepidemiological tables. To this end,
a subcommittee was formed consisting of members of the Council’s Committee on an
Assessment of the Centers for Disease Control and Prevention Radiation Programs and other
experts. The proposed study began in May 2000. The goal was to produce a consensus report by
August 23, 2000. A slate of subcommittee members was developed that contained nine nominees
(the chair and four members of the parent committee and four new members) with expertise in
epidemiology, risk assessment and risk communication, computational modeling, and statistics
(especially in uncertainty analysis). The tasks set before the subcommittee were (1) to assess the
utility of the data sources used by the radioepidemiological tables working group in its
preparation of the revised tables, (2) to evaluate the assumptions implicit in the tables concerning
radiologic effects, (3) to consider the epidemiologic and biostatistical methods used in the
revision, (4) to appraise the means by which uncertainty was handled, and (5) to provide advice
on how the tables should be made available to the public. By this report the National Research
Council is transmitting the results of the review of the working group’s draft report that the

subcommittee conducted in fulfillment of these tasks.

The original tables (NIH, 1985) were mandated under Public Law 97-414 (the “Orphan
Drug Act”) and were intended to constitute a means to estimate the likelihood that a person who
has or had any of several radiation-related cancers developed cancer as a result of exposure to
ionizing radiation from the nuclear-weapons tests in Nevada. The mandate included provision
for periodic updating of the tables. The motivation for their current revision reflects the
availability of new data (especially on cancer incidence), of new methods of analysis, and of the

need for a more thorough treatment of uncertainties than was attempted in the original tables.


APPENDIX A

Specific Comments on the Working Group’s Draft Report

p. 13, line 2: Clarify whether the excess risk, excess relative risk, or. some other measure was

assumed to be lognormally distributed over time since exposure.
p. 13, section 5, line 5: The reference to section IJ.C.1 appears to be incorrect.
p. 14, line 3: Why would risk 5-14 years after exposure have been overestimated?

p. 16, section B, last sentence: Is there any reason to be concerned about possible differential

ascertainment by calendar time or dose?

p. 20, section 2 ff: Although the discussion of whether radiation accelerates the appearance of
cancer or increases the risk is relevant to some of the points made by Robins and
Greenland, a more fundamental issue is the bias in AS as an estimator of the mean of
individual PCs produced by heterogeneity in baseline rates. This point is discussed
elsewhere in the National Research Council Oversight Committee’s report, but merits

some discussion in the working group’s report.

p. 29, lines 2-3 after the formula: The g following B should be omitted because it is not the

parameter, but the observed covariate value (sex).

p. 32: In analyzing the Japanese atomic-bomb survivor data, very little uncertainty in neutron
RBE is allowed. That seems incongruous with the current debate over neutron RBEs in

55


Pearl J. Causality: Models, Reasoning and Inference. Cambridge, UK: Cambridge University
Press, pp. 283-308, 2000.
Robins J, Greenland S. The probability of causation under a stochastic model for individual risk.
" Biometrics 45: 1125-1138, 1989.
Robins J, Greenland S. Estimatibility and estimation of expected years of life lost due to a
hazardous exposure. Stat Med 10: 79-93, 1991.
Ron E, Modan B, Preston D, Alfandary E, Stovall M, Boice J. Radiation-induced skin
carcinomas of the head and neck. Radiat Res 125:318-325, 1991.
Ron E, Lubin JH, Shore RE, Mabuchi K, Modan B, Pottern LM, Schneider AB, Tucker MA,
Boice JD Jr. Thyroid cancer after exposure to external radiation: a pooled analysis of
seven studies. Radiat Res 141: 259-277, 1995,
Ron E, Preston DL, Kishikawa M, Kobuke T, Iseki T, Tokuoka S, Tokunaga M, Mabuchi K.
Skin tumor risk among atomic-bomb survivors in Japan. Cancer Caus Cont 9:393-401,
1998.
Scotto J, Fears T, Fraumeni J. Incidence of nonmelanoma skin cancer in the United States. In:
U.S. Dept. Health and Human Services: 113 pp. Vol NIH Publ. No. 83-2433, 1983.
Scotto J, Kopf A, Urbach F. Non-melanoma skin cancer among caucasians in four areas of the
United States. Cancer 34:1333-1338, 1974.
Shafman TD, Levitz S, Nixon AJ, Gibans LA, Nichols KE, Bell DW, Ishioka C, Isselbacher KJ,
Gelman R, Garber J, Harris JR, Haber DA. Prevalence of germline truncating mutations
in ATM in women with a second breast cancer after radiation therapy for a contralateral

tumor. Genes Chromosomes Cancer 27:124-129, 2000.

71


line with the reason why kidney cancer should be considered for compensation at all: although
the data on kidney cancer are somewhat equivocal and provide no significant evidence of a dose-
response relationship, there is no evidence that it differs broadly in radiogenicity from other solid
tumors. Placing it on the list of allowed tumors is not based purely on the data on this one site,
but rather is informed by our general perceptions concerning solid tumors as a whole.
Correspondingly, the upper confidence interval should not be based purely on the data for this

one site, but should also reflect the same general findings.

We recognize that the above analysis is grossly simplified, since effects of age at exposure
and time since exposure clearly will complicate a complete hierarchic analysis. Nevertheless, we
feel that it is important in the case of rare tumors for the data on specific sites to be supplemented
by what is known about other sites, and we believe that a random-effects analysis provides a
reasonable, albeit largely pragmatic general framework. Accordingly, we suggest that the
Working Group consider a hybrid analysis conducted as follows: a single summary ERR risk
estimate for each of the nonradiogenic-cancer sites should be prepared as in table X of
Thompson et al. (1994). A random-effects analysis of the summaries could be conducted (by the
methods of DerSimonian and Laird) to combine broadly homogeneous sites (such as all non-
hormone-related solid tumors). For a given site, the ratio, r, of the breadth of the confidence
interval from the random-effects analysis compared to that from the analysis conducted
separately for the site would be calculated (for kidney cancer, r = 0.28). This factor, r, then
should be used as a deflation factor to reduce the range of confidence intervals derived for age-

specific estimates of ERR by using the data on that tumor alone.

The subcommittee believes that the hybrid analysis is a useful strategy and has a wide
application. Although there may be other approaches as well, the important thing is to find a way

to deal with the anomalies that the exceedingly broad confidence intervals present.

4l


11. CONCLUSIONS AND RECOMMENDATIONS

Utility of Data Sources

Conclusion

¢ The working group has consistently used the best sources available on the magnitude of
radiation effects, modifiers of radiation risk, baseline rates of disease, and other factors.
When sources were unavailable or inadequate, it has made reasonable judgments in modeling
parameters and their uncertainties. Risks of lung cancer posed by radon could not be

assessed from the atomic-bomb survivor cohort, but a well-established risk model based on

many cohorts of underground miners is available (NRC, 1999).

Recommendations

e Sources of data for modeling uncertainty in the RBE for neutrons and modeling the

systematic biases in the gamma and neutron dose estimates need to be explained and

discussed.

e A module for computing ASs for lung cancer in radon-exposed people would be a valuable

addition, as opposed to waiting for the next update.

© The basis of the uncertainty distributions for the DDREF should be described and justified.

43


3. CRITIQUE

Utility of the Data Sources

Incidence data from the study of the Japanese survivors of atomic bombs were used to
define risk coefficients per unit dose of radiation for various cancer types. The strength of these
data include: the exposed group is large and duration of follow-up is long, there are organ-
specific dose estimates for individuals, there was a wide range of doses, there is appreciable
information in the low-dose range (most of the subjects received doses of less than 10 cGy), all
organs and tissues were irradiated, people of all ages at the time of exposure were included in the
study, outcomes were not biased by medical conditions that would have prompted irradiation,
and medical follow-up was of high quality. The Japanese study is not without its limitations,
however: estimates of the denominator for the tumor-registry data are imprecise (only
approximate estimates of the size and composition of the atomic-bomb population still residing
in the catchment area of the Hiroshima-Nagasaki tumor registries are known), cancer-incidence
data are more subject to effects introduced by the manner in which the surveillance is conducted
than cancer-mortality data, dose uncertainty might affect the risk estimates in unknown ways and
degree, the magnitude of the neutron component of the exposures is uncertain, and there was no
dose fractionation or dose protraction, as there would be in typical occupational exposures.
Those caveats notwithstanding, the subcommittee views the data resulting from the studies of the
Japanese survivors of atomic bombs as an acceptable basis for estimating risk now. In the future,
however, a broader-based radiation risk assessment should be used for tumor sites for which
there are other high-quality sources of data available for estimating risk. The inclusion of data
on exposed Western populations and groups with fractionated or protracted exposures will lessen

reliance on the “transport” and the dose and dose-rate effectiveness factor (DDREF).

The Working Group's report includes all cancer sites addressed in the 1985
Radioepidemiological Tables except bone cancer associated with injection of 7Ra and lung

cancer associated with inhalation of radon decay products. No explanation is provided for either

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