Model Uncertainty in Cancer Risk Assessment
Edward J. Calabrese, Ph.D. , Dima Yazji Shamoun, Ph.D.
1
2
University of Massachusetts, Amherst, MA, 2Mercatus Center at George Mason University, Arlington, VA
1
Introduction
In Search of Model Uncertainty
Scientific limitation and its magnitude is an important concept that we communicate through uncertainty characterization. Inadequate model uncertainty characterization distorts cancer risk figures, thus misleading decision-makers
who use these risk figures to compute the magnitude of
the benefits and costs of health regulations.
Thus, the focus of this paper is on model uncertainty characterization in cancer risk assessment for extrapolation of
the dose-response relationship from high- to low-dose. We
document federal guidelines on model uncertainty from
the early 1980’s to the present. Our method incorporates
the various federal guidelines and current agency practices of computing potency while taking into account competing findings in the scientific literature.
Objectives
Advances in our understanding of the nature of the dose
response curve in the low-dose region have not made their
way into regulatory science.
While proper model uncertainty characterization is essential in risk assessment in general, the particular zero-tolerance regulatory policy for carcinogens makes it essential
for cancer risk assessment—if we are to communicate to
the public the true risks involved and thus an accurate account of the net benefits involved in regulatory action.
The aim of this project, therefore, is to provide a framework for characterizing model uncertainty in a manner consistent with federal guidelines and agency practices. Our
suggested framework is transparent and objective. Account must be given of the probability distribution assigned
(i.e., the weight of emphasis given to each model) and the
range of evidence used to convey information on different
carcinogens.
We propose the following:
Figure 2: Characterizing True Uncertainty: Model Uncertainty
Figure 3: Time Trend of Model Uncertainty in the Literature and Federal Guidlines
The blue line indicates the time trend of the probablity of the words
"model uncertainty" showing up in the English corpus according to
Google Ngram. The red line offers a basis for comparision. Each vertical black line denotes a major federal document or guidance released in cancer risk assessment. Model uncertainty has been hinted at ever since the issuance of the Red Book in 1983 and the words
"model uncertainty" began being used in 1994 with the release of
NRC's Science and Judgement in Risk Assessment.
Suggested Method
Given some hypothetical carcinogen, Figure 1 below outlines a common practice of computing model uncertainty.
This method accomplishes two goals: the first is to consider the
wide disparity in scientific knowledge; the second is to explicitly
communicate the weight of evidence methodology used. We can
continue the pratice of weighing all studies equally when consensus
does not prevail (Figure 1 ) while still considering evidence from different models of low-dose response (Figure 2).
Conclusions
Risk figures are crucial because they translate into numbers of lives saved, making model uncertainty an integral
part of cancer risk assessment. Overly conservative assumptions (e.g., LNT) can inflate the benefits associated
with regulatory action. Once model uncertainty is taken into account, and a range of risk is estimated, we can have
a better idea of whether a regulatory action constitutes a
net benefit—or a net cost—to society.
References
Figure 1: Uncertainty Characterization Common in Regulatory Practice
Given low-dose linearity (LNT), potency distribution is created using the different benchmark doses arrive at in animal studies. From
x studies, x/2 estimated that the slope of the LNT model is 0.003 and
x/2 estimated that the slope is 0.004, weighing all studies equally.
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