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Lung cancer: interaction of radiation exposure with smoking. What to do in IREP?

David J Brenner 

Columbia University 

djb3@columbia.edu 

June 28 2005 

 The issue here is how to estimate age-specific values of lung cancer ERRat a given dose for 

the US population, in the presence of large differences in baseline rates caused by smoking status. 

The NCI approach, documented in the 2003 NCI-CDC report on revision of the NIH 

Radioepidemiological Tables (Land et al. 2003), differs from the NIOSH-IREP approach in the 

balance that is chosen between the additive and multiplicative models. 

Both codes use a linear combination of an additive and a multiplicative model: 

(ERR)US = y×(ERR)mult + (1-y) × (ERR)add , 

where the variable y varies between -0.1 and 1.1. Here, (ERR)mult is the site-, sex-, and agespecific excess relative risk obtained from the A-Bomb data, and (ERR)add is the same value, 

adjusted for the corresponding ratio between baseline rates in the two countries. y=0 corresponds 

to the additive model, y = 1 to the multiplicative model. 

 The NCI approach is to sample from a 

probability distribution for y that is shown here. The 

solid line indicates that any value of y (from 0 to 1) 

is equally likely, while the dashed line, adopted for 

lung cancer, indicates that a 50% chance is given to 

pure additivity (y=0) and 50% weighting is 

distributed equally among any other value of y

between 0 and 1. (There is also a small probability 

assigned to values of y less than 0 and greater than 

1). The NCI approach was based on the results of Pierce et al (2003) in a recent analysis of Abomb survivors. 
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 By contrast, the NIOSH model uses an interaction term based primarily on radon (alpha 

particle exposure) data, sampling from a probability distribution of y values that is weighted 

towards the multiplicative. 

 For the endpoint of lung cancer, studies looking at the interaction between smoking and 

(high) radiotherapy doses have concluded that the interaction are reasonably well described by a 

multiplicative model (Neugut et al. 1994, van Leeuwen et al. 1995, Gilbert et al. 2003). Studies 

looking at the interaction between radon (alpha particle) exposure and smoking have either found 

either a multiplicative interaction (Samet et al. 1991, Pershagen et al. 1994, Morrison et al. 1998, 

Melloni et al. 2000) or an interaction which is intermediate but closer to multiplicative (Hornung 

et al. 1995, NRC 1999). One study (Tokarskaya et al. 2002) has suggested that the balance 

between additive and multiplicative models depends on the smoking level (higher smoking levels 

make the interaction more multiplicative, lower smoking levels make the interaction more 

additive). There is one recent report for A-bomb survivors of a purely additive interaction (Pierce 

et al. 2003). Specifically, Pierce et al (2003) found that, among A-bomb survivors with both 

radiation dose estimates and smoking history data, lung cancer risk was consistent with an additive 

model for interaction model between radiation and tobacco smoking, but inconsistent with a purely 

multiplicative model. 

 There is little in-vitro or mechanistic research to help guide us, though one study from Hei and 

colleagues showed an additive interaction between cigarette smoke condensate and gamma ray 

exposures (Piao and Hei 1993). 

 It is clear that the nature of the interaction between tobacco and radiation exposure is not 

known. Although the analysis from Pierce at al (2003) is solid, there are many uncertainties, in 

large part due to the limited quantification available of smoking patterns in the A-bomb survivors. 

Apart from this uncertainty, we do not know whether the interaction patterns found there (in a 

Japanese population with a mean date of birth approximately 1910) can be used for a US 

population with any more confidence than the radiotherapy patterns (Neugut et al. 1994, van 

Leeuwen et al. 1995, Gilbert et al. 2003) where the mean date of birth is more recent, the 

population is Western, the smoking data are rather more complete, but the radiation doses are 

higher. 
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 In summary, this Reviewer suggests that the overall weight of evidence suggests that the 

interaction is indeed intermediate between additive and multiplicative, and we really cannot say 

more than that. 

 On a practical level, therefore, it does not seem reasonable to use, for the primary IREP 

model, the NCI model which weights the balance much more towards additive. This Reviewer 

suggests that, at the present time, a model in which all values of y between zero and 1 are equally 

likely represents the most reasonable approach for probability of causation estimates. It would 

certainly be reasonable to include, within 95% confidence limits, a model which is 50% additive 

(y=0) and the rest of the probability distributed equally between y=0 and y=1 (the current NCIIREP model), as well as a model which is 50% multiplicative (y=1) and the rest of the 

probability distributed equally between y=0 and y=1. It is suggested that the “tails” below 0 and 

above 1 be removed. 

 Thus the suggestion is that the “extreme” IREP models are not purely additive or purely 

multiplicative, but somewhat intermediate. It is suggested that risk estimates from all three 

model be included in IREP outputs, with appropriate annotations for each. 

A problem with the IREP lung model(s) as they stand is the lack of documentation as to exactly 

what is done. A user of IREP, either the NCI or the NIOSH version, really would have no idea 

what or which model is being used, a quite unfortunate state of affairs. This really needs to be 

rectified. 
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