USC APR 3.0 ono)

UNIVERSITY
OF SOUTHERN
CALIFORNIA.

Division of
Biostatistics

Department of

Preventive Medicine
April 26, 2001

Larry J. Elliott, MSPH, CIH
Acting Director, Office of Compensation
Analysis and Support
National Institute of Occupational Safety and Health
Robert A. Taft Laboratories
4676 Columbia Parkway
Cincinnati, OH 45226-1998

Dear Dr. Elligh, Lorry.

Thank you for the opportunity to comment on the development of the Energy Employees
Occupational IlIness Compensation Act of 2000. As you probably are aware, my colleague at
USC, Duncan Thomas, and I served on a recent National Academy of Sciences Committee that
reviewed a draft report of the NCI-CDC Working Group to revise the 1985 Radioepidemiological
Tables. Our committee’s report was recently released and is available from National Academy
Press. I would like to comment upon several aspects of that endeavor which may be relevant to
the EEOICA. In particular our report discusses a number of issues related to the construction of
“probabilities of causation” (PC) from epidemiological data and their use in the compensation
arena.

First of all one should acknowledge that the concept of the probability of causation is somewhat
slippery. This was recognized by an earlier NAS review of the original 1985 Tables, which
suggested the use of a different name for the concept, i.e. “assigned share” or AS. This choice of
name reflected, in part, the fact that unlike a true probability, the usual definition of PC = (RR-
1)/RR, may be negative (if an exposure is actually beneficial). There is a more important aspect
of the failure of the PC to behave like a probability, however. That is that PCs do not in general
“average out” the way ordinary probabilities do. That is if there is a population of exposed
individuals each one of who are unique in terms of their baseline disease rates and in their
susceptibility to exposure then the average of the individual PCs of persons with disease, will not
generally be equal to (RR-1)/RR as estimated from an epidemiological study. Specifically if each
individual has a different background probability of disease it is possible to construct examples in
which the personal probability of causation for everyone with disease is arbitrarily close to one,
but the estimate obtained from an epidemiological study can be arbitrarily close to zero. In fact, a
University of number of statistical articles (Robins and Greenland, 1989, 1991, Greenland 1999) have gone into

sete aoe at deal of detail concerning this failure of the PC to average over groups of heterogeneous
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individuals, and take this as a rationale for abandoning its use in compensation and in the settling
of litigation. Further these articles make the generic statement that no epidemiological study can
address the variation in individual disease risk and thus even the most extreme models for
heterogeneity in baseline risk cannot be invalidated by epidemiological data. I point out this issue
to alert you to the fact that the use of probability of causation for compensation and litigation
purposes is the subject of heated current debate in some quarters.

As a committee, then, we were faced with a basic question about whether the AS estimated from
epidemiological data actually has any usefulness at all. Our general approach towards this
critique of the PC was to point out that the extreme failures of the PC (to average over
individuals) posed by Robins and Greenland are in fact very extreme in their assumptions about
the variability of individual baseline disease risk and to also take issue with their view that no
epidemiological study can inform us about variability of individual risk. We specifically pointed
out that comparisons of the correlation in disease outcome between monozygotic and dizygotic
twins and between dizygotic twins and other siblings can be used to estimate frailty models with
both general “genetic” and “shared environment” components, i.e. to estimate the heterogeneity
of individual disease risk due to genes and any environmental factors for which exposures are
more similar for twins than they are for other siblings. Duncan Thomas performed an interesting
simulation as part of preparations for a debate with Sander Greenland on this topic that was part
of the ASA 2000 radiation and health conference and is also summarized in our report. Based
upon a model for individual variation in risk of total mortality estimated based upon Danish twins
data, a model which admitted considerable heterogeneity in individual risk, Thomas showed that
the correlations between true individual probability of causation and population estimates (the
AS) were quite high leading to our conclusion that “the AS approach still has some validity, at
least for the relative ranking of claimants”.

There was some discussion in our report of the limitations of the PC (even if it can be estimated)
for the purposes of compensation. In particular we take a very dim view of the idea that
compensation schemes should be based upon a hard and fast calculation whether for an individual
his or her estimated PC is greater than or less than 1/2. Our objections to this are partly based on
unhappiness with such an inherent discontinuity in compensation; partly because of the fact that
PC’s will always be uncertain, and partly due to a recognition that compensation should consider
degree of suffering (for example, expected years of life lost) as well as the probability that the
illness is cause by the exposure. Compensation on the basis of an individual PC > 1/2 may be
required based on tort law, when cases are under litigation, however once it has been agreed that
a class of individuals are entitled to some compensation the value of this specific threshold is
voided.

The 1985 Radioepidemiological tables only included cancer sites for which the Working Group
of that time deemed to be clearly related to radiation exposure (p<0.05 in the A-bomb or other
relevant data). The 2000 Working Group chose to include many more cancer sites than just those
that had definitively been shown to be radiogenic by this criterion. Our committee recognized the
logic of including additional sites based on the perception that broad classes of similar tumors are
likely to be similarly radiogenic even if some of them are so rare in the Japanese population that
increased hazards couldn’t be clearly demonstrated in the A-bomb survivors’ data for that site
when analyzed separately. In fact we provided a “meta-analysis” of a very broad class of tumors
(consisting of all solid tumors which are not “hormonally related”) and showed that there is
relatively little evidence in the A-bomb survivors data of differences in excess relative risk
between sites based upon published A-bomb survivor risk estimates (Thompson et al).


While our committee supported in principle the idea of expanding the list of tumors for which AS
calculations were provided there was one important concern that we felt had not been addressed
in the Working Group report: specifically, how to properly incorporate sampling variability into
the uncertainty calculations when dealing with rare tumors. In particular our concern was related
to our understanding of how the compensation program established by the VA now works. In
this scheme (as I understand it), based upon an individual’s estimated exposure, an upper
confidence limit (at the a=0.01 level) for the AS is calculated — in this calculation a variety of
uncertainty factors are considered — and a case is moved forward for compensation on the basis of
whether this upper limit is > 1/2, The problem with including the rarer tumors in this setting is
that the mere fact that the tumor is rare means that estimates of the excess risk (and hence AS) are
highly uncertain, with this in tum implying that confidence limits may reach to very high levels
even for very small doses in certain instances. We suggested the use of pooled analyses (and
proposed a specific random effects-based approach) for the calculation of confidence intervals so
as to minimize the “rewarding of ignorance” in the calculation of the upper confidence limits for
the AS calculation in the case of rare diseases. Whether and how to deal with compensation for
diseases where no significant dose response is seen when analyzed separately; but also for which
little evidence exists that the dose response differs from that of other broadly similar diseases, is a
delicate one, and probably should be considered anew by NIOSH.

T hope that these general comments are useful to you.

Sincerely,

Del O fom

Daniel O. Stram
Associate Professor

References:

A Review of the Draft Report of the NCI-CDC Working Group to Revise the 1985
Radioepidemiological Tables. National Academy Press, Washington DC, 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. Estimability and estimation of expected years of life lost due to a
hazardous exposure. Stat Med 10: 79-93, 1991

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. 1999 89:1166-9

Thomas, DC. Resolved: the probability of causation can be used in an equitable manner to
resolve radiation tort claims and design compensation schemes. Pro. Radiat Res. 2000 154:717-
8.