1

HOW SHOULD NIOSH ESTIMATE RISK OF LUNG CANCER 

IN WORKERS COVERED UNDER EEOICPA 

IN THE FACE OF UNCERTAINTIES IN 

THE INTERACTION BETWEEN SMOKING AND LOW-LET RADIATION 

SENES Oak Ridge, Inc. 

102 Donner Dr. 

Oak Ridge, TN 37830 

August 26, 2005 

BACKGROUND 

The NCI-CDC Working Group revised the NIH-IREP lung cancer model in response 

to a report on analyses of the joint effects of radiation and cigarette smoking on lung 

cancer risk among the Japanese atomic bomb survivors (Pierce et al. 2003). The IREP 

model allows for interaction on the relative rate scale between radiation dose and 

cigarette smoking with an uncertainty distribution for an “adjustment factor” that 

accommodates interactions ranging between additive, multiplicative, or supermultiplicative (Apostoaei and Trabalka 2004). In the NIH-IREP lung model the 

distribution for this adjustment factor is more heavily weighted toward an additive 

interaction between radiation dose and cigarette smoking (as compared with the NIOSHIREP model) and alternate parameterizations of the risk coefficients, derived as a result 

of the analysis by Pierce et al. (2003), are employed, such that never-smokers are the 

reference group (rather than an aggregated population of smokers and non-smokers, as is 

the case for NIOSH-IREP). Consequently, the same inputs entered into NIH-IREP and 

NIOSH-IREP produce significantly different probabilities of causation for some exposure 

profiles. Apostoaei and Trabalka (2004) recently recommended that NIOSH adopt the 

NIH-IREP model for lung cancer. However, since there are categories of people for 

whom NIH-IREP is less friendly, Apostoaei and Trabalka (2004) also suggested that 

NIOSH could consider programming NIOSH-IREP to run both the new NIH-IREP lung 

model and the current NIOSH-IREP lung model and choose whatever probability of 

causation is larger for a given individual. 
2

To summarize the results of the Pierce et al. (2003) study that led to the approach 

used in NIH-IREP: They analyzed the joint effect of smoking and external radiation 

exposure on lung cancer risks in the LSS cohort and found that they were significantly 

sub-multiplicative and consistent with an additive model. There was a weak suggestion in 

the LSS data that light smoking tended to act multiplicatively with radiation while heavy 

smoking appeared to act additively. Given that 84% of men but only 16% of women 

were smokers, they found that an additive adjustment for smoking reduced the 

female:male ERR/Sv ratio to values comparable to female:male ratios normally observed 

for other solid tumors. With adjustment for smoking, there was evidence of a decline in 

the ERR/Sv with increasing attained age (comparable to other cancer sites), but no 

evidence of modification by age at exposure. 

This led the BEIR VII committee to conclude that Pierce et al. (2003) demonstrated 

that inferences about the modifying effects of gender and age at exposure on the ERR can 

be distorted if analyses do not account for an additive interaction between radiation and 

smoking, because smoking habits in the LSS cohort depend strongly on these factors. 

They noted that, in contrast to the results from the LSS cohort, studies of underground 

miners exposed to radon (NRC 1999) or of Hodgkin’s disease patients treated with high 

doses of radiation (Gilbert et al. 2003) rejected additive interactions and found that 

multiplicative interactions were compatible with the data. However, they also concluded 

that these studies may be less relevant for estimating the risks of low doses of low-LET 

radiations than those of the LSS cohort because (1) underground miners were exposed to 

alpha-emitting radon progeny and (2) the evidence for a multiplicative relation of 

radiation and smoking comes primarily from analyses of miners in Colorado and China 

where lung doses (in Sv) were much higher than in the LSS cohort (NRC 1999). 

Although miner data were compatible with a multiplicative effect and not an additive 

one, the estimated interaction was super-multiplicative (i.e., akin to the interaction model 

used in NIOSH-IREP for all types of radiation and in NIH-IREP for exposures to alpha 

emitters). Hodgkin’s disease patients were given very high doses (mean 25 Gy to the 

lung) and, in addition, were subject to the immunodeficiency inherent with lymphoma 

and associated with subsequent chemotherapy that was given to many of the patients 

(NRC 2005). 
3

The additive adjustment for smoking in the LSS cohort made by Pierce et al. (2003) 

reduces the female:male sex ratio in the ERR/Sv to 1.6, which is comparable to the sex 

ratio in the ERR/Sv for other solid cancers (1.7). For the EAR/Sv for lung cancer 

estimated by the BEIR VII committee without information on smoking, the female:male 

sex ratio is 1.5 (NRC 2005; see additional discussion below). In addition, for many solid 

cancers those who were at a young age at radiation exposure have greater values of 

ERR/Sv than those who were older when exposed, but for lung cancer this effect is 

reversed if an adjustment for smoking is not made. Although it was possible to 

understand these atypical patterns without explicit adjustment for smoking (i.e., because 

it was well-known that far more males than females were smokers), the results from the 

analysis by Pierce et al. (2003) indicated that lung cancer radiation risks, adjusted using 

an additive radiation-smoking interaction, could have sex and age-time patterns similar to 

those seen for solid cancers that are not strongly related to smoking. 

The BEIR VII committee did not use smoking-adjusted risk coefficients from the 

LSS in its analyses (NRC 2005). Rather, they applied adjustments for both age at 

exposure and attained age to risk coefficients for lung cancer that were developed from 

their analysis of all solid cancers (excluding thyroid and non-melanoma skin cancers). 

The resulting female:male sex ratio in the ERR/Sv was 4.4, while that for the EAR/Sv 

was 1.5, as noted above. They dealt with the issue of smoking and the differences in the 

sex ratios by applying a weight of 0.7 to the EAR-based estimate and 0.3 to the ERRbased estimate (NRC 2005), citing the evidence from the study by Pierce et al. (2003) in 

support of their decision. If background incidence rates for males and females are equal, 

this approach effectively reduces the overall female:male sex ratio in such a combined 

risk estimate to 2.3, a value that is closer to the value of 1.6 obtained by Pierce et al. 

(2003) when the ERR/Sv alone was adjusted for smoking. 

In essence, the results from the approach used by the BEIR VII committee is 

comparable to what would result if a sub-additive smoking interaction had been 

employed as an adjustment to the ERR/Sv alone. The net effect of this “correction” for 

additivity on effects of smoking is thus more comparable to the net effects of the 

combined approaches for risk transport and smoking interaction used in NIH-IREP than 

in NIOSH-IREP. 
4

To obtain a formal review of the NIOSH-IREP and NIH-IREP lung models, NIOSH 

approached a number of experts in the field epidemiology and radiation risk assessment 

and received evaluations from: David Brenner, Faith Davis, David Richardson, and 

Jonathan Samet. The reviewers were asked to respond to three basic questions: 

• In your expert scientific judgment, should NIOSH adopt the NIH-IREP lung cancer 

risk model for exposures other than radon for use in NIOSH-IREP? 

• If so, should the model be adopted intact, or should NIOSH modify it in some way to 

better fit the characteristics and radiation exposures of nuclear weapons workers 

covered under EEOICPA? 

• Alternatively, should NIOSH-IREP be programmed to run both lung cancer models 

and to output only the higher probability of causation? 

It should be noted that the reviewers did not have access to the final BEIR VII report, 

because it was not made available until late June 2005. However, Jonathan Samet 

reportedly reviewed the report prior to its release. 

SUMMARY OF EXPERT OPINIONS 

None of the reviewers recommended that NIOSH use the current NIOSH-IREP lung 

model exclusively. One reviewer (Faith Davis) recommended that NIOSH-IREP model 

be dropped and NIH-IREP lung model be adopted intact because she favored the 

alternative parameterization of risk coefficients and heavier weighting of an additive 

interaction between smoking and external radiation (derived from the analysis by Pierce 

et al. 2003) that was used in NIH-IREP. Contrary to the views expressed by Davis, there 

seems to be an agreement among Brenner, Samet, and Richardson that the interaction 

between smoking and radiation suggested by the analysis by Pierce et al. (2003, despite 

its attributes, may not fully characterize the uncertainty in this interaction. A variety of 

reasons are given, including potential lack of comparability of smoking patterns and 

effects of smoking on lung cancer in Japanese males compared to the U.S. population, 

potential time dependencies in smoking-radiation interactions, birth cohort effects, 

limited numbers of individuals exposed at older ages, and lateness of follow-up initiation, 
5

commencing about 19 y after exposure, in the Japanese A-bomb survivors. Two of these 

reviewers (David Richardson and Jonathan Samet) suggested running both NIOSH- and 

NIH-IREP lung models. The last reviewer (David Brenner) suggested using the NIHIREP model, but with an interaction between radiation and smoking equally weighted 

between the additive and multiplicative interactions, and proposed that three radiationsmoking interaction models be included in IREP outputs. The specific opinions of these 

experts in support of their conclusions are discussed in the next sections. 

Faith Davis

The case for use of NIH-IREP as the only model for estimating lung cancer risk in 

nuclear weapons workers covered under EEOICPA was made by reviewer Faith Davis. 

Her rationale was the following: 

• The NIH-IREP model provides estimates of lung cancer risk based on four additional 

years of follow-up from the RERF cohort and, as such, estimates more closely 

approximate current risks and are an improvement over those currently used in the 

NIOSH-IREP model. 

• The inclusion of age is an important feature of the revised NIH-IREP model and 

would be a very important modification to the NIOSH-IREP model. The process of 

carcinogenesis has fundamental age dependencies that have been repeatedly shown in 

both animal and human data (Armitage and Doll 2004). Pierce et al (2003) 

demonstrated the difficulty of separating out the effects of attained age and age at 

exposure, yet clearly demonstrated the potential importance of both. 

• The confidence intervals around the probability of uncertainty estimates from the 

NIH-IREP reflect multiple important factors, including: updated risk estimates from a 

subset of the RERF cohort, a change in the number and form of parameters in the risk 

model, and a change in the uncertainty estimation approach. Each change appears to 

have been well reasoned by Pierce at al. (2003). The fact that estimates of the 

ERR/Sv from this model in nonsmokers closely approximate those expected for other 

radiogenic tumors results lends credence to this new lung cancer model. The 

declining ERR/Sv across smoking levels and the large confidence limits among 

smokers reflects the interactive effects of radiation and smoking and the dominant 
6

effect of smoking on lung cancer risk. As such, the narrower confidence intervals 

among non-smokers and wider confidence intervals among smokers in the NIH-IREP 

model versus the NIOSH-IREP model are scientifically sound and reflect a 

substantive improvement. 

• The confidence bounds for males and females among never smokers are, for the most 

part, narrower in the NIH-IREP model compared to the NIOSH-IREP model across 

both age constructs. This suggests that the adjustment by sex, in combination with 

the introduction of the age parameters in the NIH-IREP model, is providing more 

robust estimates which may better reflect the underlying biological processes for 

these tumors. 

• The use of never smokers as the reference category in the NIH model, seems a more 

accurate reflection of smoking risks than the averaging procedure used in the NIOSH 

schema. 

• As the NIH-IREP model reflected the best available estimates relevant to this 

exposure population, she recommended implementing this model and discontinuing 

the use of the NIOSH-IREP model: To use both models for the indefinite future 

would set a difficult precedent for that point in the future when better estimates than 

these become available. 

• She concluded that at this time the NIH-IREP model provides better estimates than 

the NIOSH-IREP model from several perspectives – they are based on more recent 

risk estimates, a newer interaction distribution with respect to radiation and smoking 

is incorporated and better reflected across smoking categories; and most importantly, 

estimates are specific to age. She also concluded that estimates from this model are 

well reasoned both biologically and statistically and should replace the prior model. 

Jonathan Samet 

While agreeing with the conclusions of Pierce et al. (2003) with respect to rising risks 

for lung cancer associated with an increased level of smoking in Japan since World War 

II, Samet pointed out that the temporal profile of smoking in the LSS cohort (an 

unknown) and its age-birth cohort dependence complicate interpretation of models with 
7

time-dependent factors (such as those used by Pierce et al. 2003). Other important points 

included the following: 

• Extension of the follow-up period by four years would represent only a modest 

increment to the LSS database in terms of follow-up time and numbers of lung 

cancers that should not have a significant effect on risk estimates. 

• Information on smoking in the LSS cohort was limited and only limited attention was 

given to effects of measurement error in assessment of smoking patterns and to the 

possibility of age and birth-cohort dependent measurement error. 

• Inadequate model specification could potentially bias assessment of the smokingeffect modification toward additivity. 

• There was evidence of different smoking patterns and lower lung cancer risks at any 

level of smoking among Japanese males compared to the U.S. population.1

David Richardson 

Richardson concluded that the epidemiological literature provides a very limited 

scientific basis for characterizing the joint effects of cigarette smoke and external 

exposure to ionizing radiation on lung cancer risk. He also raised the most detailed 

objections to exclusive use of the NIH-IREP model. His major points follow: 

• NIOSH is concerned with appropriately characterizing the interaction on a relative 

rate scale between two time-varying continuous exposure variables (i.e., occupational 

radiation doses and cigarette smoking rates). In contrast to the statistical evaluation of 

product terms for two fixed binary exposure variables, an evaluation of the interaction 

between smoking history and occupational radiation exposure history may be 

relatively complex. Statistical evidence of a smoking-radiation dose interaction may 

depend upon model assumptions about the etiologically-relevant periods of exposure 

for each agent, assumptions about the importance of the temporal ordering of 

exposures, and assumptions about the shapes of the exposure-response patterns for 

single versus joint exposures. Misspecification of such assumptions may lead to 

incorrect conclusions about the nature of the interaction between exposures. 

 

1

 The last part of this conclusion is not fully supported by the reference materials supplied by Samet and 

seems to be refuted by data in Table 3 of Pierce et al. (2003). 
8

• The situation considered by Pierce et al. (2003) is somewhat simpler than that of 

direct concern to NIOSH since in the case of the study of atomic bomb survivors 

radiation dose is considered as a fixed variable. Subjects were treated as continuous 

smokers (at a fixed rate based upon the response to at least one survey about cigarette 

smoking) or never smokers. They do not consider age at start of smoking or its 

relation to age at time of bombing; they do not consider age at termination of 

smoking or its relation to age at onset of cancer; and, they do not consider the 

duration of smoking or cumulative pack-years. Their cited reasons for this 

simplification included the analytical difficulties of utilizing information on each 

person’s smoking history and the limitations of the available smoking history data. 

• A fundamental question, however, is whether the investigators’ simplified approach 

to treating the temporal aspects of joint exposures have led to spurious conclusions 

about the true nature of the joint effects of these agents.2

 Mechanistic models and 

experimental evidence for some carcinogen pairs suggest that the temporal patterns of 

exposures and subsequent disease risk are fundamental considerations when trying to 

appropriately characterize the joint effects of the exposures. In the case of cigarette 

smoking, prior epidemiological research has established that the temporal aspects of 

smoking history are important considerations when evaluating smoking effects on 

lung cancer risk (Brown and Chu 1987; Doll, Peto et al. 2004). It is reasonable to 

postulate that the temporal aspects of smoking history may be as important or more 

important to analyses of the joint effects of smoking and radiation dose on lung 

cancer risk. 

• The study question investigated by Pierce et al. requires relatively complete and valid 

information on individual survivor’s smoking histories. The survey data used in this 

analysis, in contrast, provides snapshots in time of smoking histories for those who 

responded to mail questionnaires or clinic visits. Such information is highly 

incomplete (missing entirely for about 45% of survivors); for those with some 

information on smoking history, information analyzed on smoking rate pertains to the 

average smoking rate reported at one or more survey dates. No information in these 

 

2

 This concern was echoed by Jonathan Samet, as noted earlier. 
9

analyses characterizes the potential variation in smoking rates over decades of life 

prior to the atomic bombing or, in fact, prior to first survey in the 1960s. 

• As in any epidemiological analysis based on self-reported information it is reasonable 

to question the reliability of this information. The authors state that about 2400 (i.e, 

roughly 5%) of those who responded to at least one mail or clinical survey provided 

incomplete or inconsistent information and had to be excluded from analyses. It is 

likely that other survivors provided information that was inaccurate but not logically 

inconsistent and therefore was included in these analyses. 

• Crucially, the analyses reported by Pierce et al. are constrained to provide information 

on the nature of the smoking-radiation dose interaction only for the period that begins 

approximately two decades after radiation exposure. If the joint effects of radiation 

and smoking exposures on lung cancer risk were relatively largest in these early years 

after radiation exposure, and diminished with protracted time since exposure, then the 

analysis by Pierce would mischaracterize their joint effects due to the limitation of 

only examining later periods of follow-up. 

• The requirement that persons be alive and cancer-free at the time of the smoking 

surveys also has implications for evaluations of variation in ERR/Sv with age-atexposure. As illustrated in Figure 3 of the paper by Pierce et al.(2003) a relatively 

small number of A-bomb survivors who were aged 50 or older at time of bombing 

were eligible for inclusion in this analysis; and a substantial proportion of these 

survivors would be classified in the lowest (i.e. referent) radiation dose category. 

• Neither the findings of Pierce et al. (2003) nor the other findings in the 

epidemiological literature provide compelling support for the conclusion that the 

radiation-smoking interaction is likely additive on the relative rate scale, as described 

by the current NIH-IREP model.3

 

• Unfortunately, neither is there is there compelling evidence to support the NIOSHIREP model. Rather, the nature of this interaction remains poorly characterized and 

the epidemiological literature provides an extremely limited scientific basis for this 

aspect of compensation decisions. A decision about how to model this interaction 

 

3

 The smoking-radiation interaction in NIH-IREP is not purely additive, but accommodates interactions 

ranging between additive, multiplicative, or super-multiplicative. Thus, the last part of this statement made 

by Richardson is incorrect (see later commentary in Discussion section). 
10

therefore becomes primarily a policy decision about how to make a compensation 

determination in the face of scientific uncertainty. 

David Brenner 

David Brenner pointed out that the interaction between smoking and high 

radiotherapy doses was reasonably well described by a multiplicative model. He also 

noted that radon studies either found a multiplicative interaction or an interaction that 

was intermediate, but closer to multiplicative. He also reported that a study of the Mayak 

workers suggested that the balance between different interaction models depends on the 

smoking level; higher smoking levels make the interaction more multiplicative, while 

lower levels make it more additive. In the Mayak workers, this pattern was thus 

essentially opposite to that for the members of the LSS cohort studied by Pierce et al. 

(2003) (as discussed earlier). 

Brenner’s final conclusion appears to capture the views of all three reviewers with 

dissenting opinions, namely that the overall weight of evidence suggests the interaction 

between smoking and external radiation is intermediate between additive and 

multiplicative, and we really cannot say more than that. Although Brenner does not 

recommend running both models to deal with these uncertainties (as do Samet and 

Richardson), he did suggest running NIH-IREP with three independent parameterizations 

of the interaction between smoking and radiation. 

Brenner deemed it unreasonable to use the NIH-IREP model which weights the 

balance much more towards an additive interaction.4

 He suggests, as an alternative, a 

model in which all values of y between zero and 1 are equally likely. He also concluded 

that it would 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 = 1,5 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. He also suggested removing the 

 

4

 Both he and Richardson apparently misunderstood the net effect of the smoking-external radiation 

interaction in NIH-IREP, which alters the balance from mainly multiplicative to weakly additive, as 

discussed in our previous evaluation of the review by David Brenner (see Apostoaei and Trabalka 2005).] 

5

 Brenner incorrectly calls this approach “the NIH-IREP model”. As we pointed out in our previous 

evaluation of his review (Apostoaei and Trabalka 2005) Brenner confused the risk transport model in NIHIREP with that used for the radiation-smoking interaction. 
11

“tails” below 0 and above 1 in this distribution, but did not provide a rationale for this 

choice.6 He also suggested that risk estimates from all three models be included in IREP 

outputs, with appropriate annotations for each. See Apostoaei and Trabalka (2005) for 

additional commentary on Brenner’s review.7

 

DISCUSSION 

It seems likely that the effects of smoking are more complicated than those of 

radiation, perhaps contributing to carcinogenesis by causing both mutations and by 

facilitating the progression toward malignancy of cells with mutations caused by other 

factors, such as radiation. This might explain the weak suggestion in the LSS data that 

light smoking tends to act multiplicatively with radiation while medium and heavy 

smoking appear to act additively. It might also explain why studies of radon exposure 

have largely indicated effects intermediate between additive and multiplicative (Pierce et 

al. 2003), but often weighted more towards multiplicative effects. 

Pierce et al. (2003) found that the best fit to the most recent LSS data is obtained if 

the radiation-smoking interaction is considered additive for all smoking categories. The 

analysis by Pierce et al. (2003) indicates that accounting for an additive radiationsmoking interaction allows the lung model to show gender and age dependencies similar 

to the dependencies observed for other solid cancers. In other words, ignoring radiationsmoking interaction, or assuming a multiplicative interaction would result in age and 

gender dependencies which appear to have much less biological plausibility. Thus, the 

additive radiation-smoking interaction used by Pierce et al. (2003) provides not only a 

better statistical fit to LSS data, but also seems to eliminate questionable gender and age 

dependencies observed in the LSS lung data. 

 

6

 There are no “tails” below 0 in the smoking-radiation interaction adjustment in either NIH- or NIOSHIREP. His reference is to the specification for the risk transport model (see footnote 5). ] 7

 It is conceivable that Brenner’s recommendations were influenced by misinterpretation of the interaction 

model in NIH-IREP. He might not have reached the same conclusions had he fully understood the nature of 

the current algorithms for the smoking-radiation interaction, which entails use of a hybrid model with 

intermediate additive and multiplicative components. 
12

Richardson, Samet, and Brenner pointed out that the analysis by Pierce et al. (2003) 

has several weak points which should be taken into account in a model based on this 

analysis. Subjects of the study by Pierce et al. (2003) were treated as either as continuous 

smokers or never smokers, and age at the start of smoking was ignored. Also, the 

smoking survey of the cohort did not start until 1964, meaning that an important number 

of early cancers were not included in the study and the age structure of the cohort 

exposed to radiation does not match the age structure of the cohort of included in the 

smoking survey. Brenner also pointed out that, when other lung cancer studies are taken 

into consideration (i.e., radon studies, Mayak workers, high-dose treatment studies) in 

addition to the analysis by Pierce et al. (2003), one could conclude that the radiationsmoking interaction can be anywhere between additive and multiplicative. The 

conclusion of these three experts is that the assumption of an additive radiation-smoking 

interaction is questionable, to say the least. 

Thus, the overall weight of evidence suggests the interaction between smoking and 

external radiation is intermediate between additive and multiplicative, and the uncertainty 

in the radiation-smoking interaction could be better represented in the lung cancer models 

in either version of IREP. 

The experts selected by NIOSH to review the lung model and the interaction between 

radiation and smoking provided the following opinions: Davis concluded that NIH-IREP 

currently provides the best characterization of lung cancer risks, two other reviewers 

(Samet and Richardson) suggest running both models and using the result that is most 

claimant friendly, and one reviewer (Brenner) suggests running NIH-IREP with alternate 

distributions to represent the interaction between smoking and external radiation, with as 

many as three different outputs. 

Given the evidence presented above, Brenner’s primary recommendation (a model for 

the interaction in which all values of y between zero and 1 are equally likely) merits 

strong consideration. The net result is a balance between the weighting of additive and 

multiplicative interactions (50%−50%) that is almost midway between that of NIH-IREP 

(65% additive and 35% multiplicative) and of NIOSH-IREP (26% additive and 

74% multiplicative). It also captures all of the central and extreme values from the 
13

alternate specifications he gave without the complexity introduced by running completely 

separate models. 

We programmed this alternative model in a research version of NIH-IREP by using a 

uniform distribution between the additive and multiplicative interactions (y = uniform 

between 0 and 1). We investigated how the 99th percentile of PC obtained using Brenner 

model would change compared to the 99th percentile of PC estimated using the original 

NIH-IREP lung model (Table 1). Since the upper bound of PC is given by the 

multiplicative interaction while the lower bound of PC is given by the additive 

interaction, the 99th percentile does not change significantly when the weight of the 

radiation-smoking interaction is shifted from 65% additive−35% multiplicative (NIHIREP code) to 50% additive−50% multiplicative (as proposed by Brenner). That is, this 

shift in weights causes a decreases the central value of PC, but the 99th percentile is 

virtually unchanged.8

While performing these tests, we also discovered that there is a difference in the 

transport between populations as implemented in NIH-IREP computer code, and the 

transport between populations as described by Land et al. (2003). In the NIH-IREP 

computer code the transport between populations is modeled using the trapezoidal 

distribution shown in Figure IV.G.1 of Land et al. (2003) that assigns a uniform weight 

between an additive and a multiplicative risk. However, Land et al. (2003) indicate that a 

50% weight was given to the additive transport model, and 50% to the trapezoidal 

distribution described above, based on the analysis by Pierce et al. (2003). That is, Land 

et al. (2003) recommends a 50% weight be given to the additive model both in the 

transport between populations and in the radiation-smoking interaction. As seen in Table 

1, the 50% weight assigned to the additive model in the model for transport between 

populations has only a minimal impact. This is the result of fact that the upper bound of 

assigned share is driven by the multiplicative model, and is relatively unaffected by the 

weight assigned to the additive model (which is responsible for the lower bound of the 

assigned share.) 

 

8

 The small fluctuations shown in Table 1 are of the same magnitude with those obtained by changing 

random seed number. 
14

An obvious concern, however, is that the risk coefficients for never smokers in the 

NIH-IREP model were derived from an analysis of the LSS data largely on the basis of 

an assumed additivity in the joint interaction of smoking and external radiation, as 

indicated by Pierce et al. (2003). According to Table 1 in Pierce et al. (2003), lower risk 

coefficients should be expected if an epidemiologic analysis is performed assuming a 

multiplicative interaction model. However, risk coefficients based on a multiplicative 

model (or on a model using an intermediate radiation-smoking interaction as 

recommended by Brenner) are not available. We don’t know at this time by how much 

the risk coefficients would be reduced, if new analyses were to be performed using 

alternate specifications of the radiation-smoking interaction. However, using a 

multiplicative interaction model instead of the additive model would result in somewhat 

lower risk coefficients, but would also introduce an average bias toward higher risks 

through changes in the radiation-smoking model and in transport between populations 

model. Thus, these two effects will compensate each other to some degree. 

Based on the recommendations of the reviewers and given the studies and data 

available to date, NIOSH is facing the choice between the following modeling options: 

1. Program the NIOSH-IREP computer code to run both NIOSH-IREP and NIHIREP lung models and report the PC produced by either model, or a PC obtained by 

combining the output of the two models. 

2. Use NIH-IREP lung model as currently coded. 

3. Use NIH-IREP lung model, with intermediate weights between the additive and 

the multiplicative models for transport between populations and radiation-smoking 

interactions. 

Whatever approach is adopted, we should ensure that the documentation of exactly 

what is done be as clear as possible to the user. The IREP lung model(s), as they stand, 

lack clear documentation (as was also recognized by David Brenner). A user of IREP, 

either the NCI or the NIOSH version, could have difficulty understanding how the 

models are being used to estimate assigned share, let alone discerning the differences 

between these two versions of IREP. 
15

Table 1. Comparison of 99th percentiles of assigned shares (probability of causation) for a person exposed to a sin

cSv at age 20 and diagnosed with lung cancer at age 40a 

MALE Acute exposure b Chronic exposure b

NIH-IREP NIOSH- IREP f NIH-IREP 

Smoking 

status 

Land et al. 

(2003)c 

NIH-IREP 

softwared Brennere Land et al. (2003)c NIH-IREP softwared Brennere 

Never smoker 51.62 52.94 52.94 53.75 50.62 53.66 53.66 Current smoker 

< 10 cigs/day 47.29 48.31 48.74 29.53 42.84 45.81 45.06 

10-19 cigs/day 47.03 47.91 48.16 25.62 42.41 44.78 44.57 

20–39 cigs/day 46.90 47.78 47.99 25.00 42.40 44.45 44.10 

40+ cigs/day 46.72 47.72 47.92 24.92 42.20 44.32 44.06 

Table continues on the next page 
16

Table 1 (continued). Comparison of 99th percentiles of assigned shares (probability of causation) for a person expo

dose of 50 cSv at age 20 and diagnosed with lung cancer at age 40a

FEMALE Acute exposure b Chronic exposure b

NIH-IREP NIOSH- IREP f NIH-IREP 

Smoking 

status 

Land et al. 

(2003)c 

NIH-IREP 

softwared Brennere Land et al. (2003)c NIH-IREP softwared Brennere 

Never smoker 68.24 70.09 70.09 76.14 67.23 69.70 69.70 Current smoker 

< 10 cigs/day 61.53 65.49 64.46 55.18 58.12 63.18 60.82 

10-19 cigs/day 60.51 64.87 63.97 51.39 57.82 62.38 59.44 

20–39 cigs/day 60.31 64.86 63.87 50.88 57.72 62.03 59.15 

40+ cigs/day 60.30 64.86 63.83 50.79 57.69 61.78 59.08 

a We performed tests for other ages at exposure and attained ages, and we obtained similar results. b Exposure to photons with energy greater than 250 keV. c Assumes 50% weight on additive model in both the transfer between populations and radiation-smoking interaction. d Assumes 50% weight on additive model radiation-smoking interaction only. The transport between populations is modele

weight between the additive and the multiplicative models. 

e Assumes uniform weight between the additive and the multiplicative model for both transport between populations and rad

interaction. 

f Assumes uniform weight between the additive and the multiplicative model for transport between populations, and a triang

weighted towards a multiplicative model for radiation-smoking interaction [y = triangular (0,1,1.1)]. 
17

EXPERT OPINIONS 

Brenner D.J. Lung cancer: interaction of radiation exposure with smoking. What to do in 

IREP? Report submitted to NIOSH; June 28, 2005. 

Davis F. Response to NIOSH questions. Report submitted to NIOSH; February 10, 

2005. 

Richardson D. Should NIOSH adopt the NIH-IREP model for lung cancer for use in the 

EEOICPA. Report submitted to NIOSH; February(?) 2005 

Samet J.M. Evaluation of two models for projecting lung cancer risk: NIOSH-IREP and 

NIH-IREP. Report submitted to NIOSH; Feb. 22, 2005. 

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commissioned by NIOSH, 27 pages, PDF file) 

Apostoaei AI, Trabalka JR. Comments on David Brenner’s recommendations to NIOSH 

regarding lung cancer model in IREP. SENES Oak Ridge, Inc., 2005 

(unpublished report commissioned by NIOSH, 3 pages, PDF file) 

Armitage P, Doll R. The age distribution of cancer and a multi-stage theory of 

carcinogenesis. Br J Cancer. 2004 Dec 13;91(12):1983-9. 

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Land C., Gilbert E., Smith J., Hoffman F.O., Apostoaei I.A., Thomas B.A., Kocher D. 

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September 2003. 

Pierce, D. A., G. B. Sharp, et al. (2003). "Joint effects of radiation and smoking on lung 

cancer risk among atomic bomb survivors." Radiation research. 159(4): 511-20. 

Gilbert ES, Stovall M, Gospodarowicz M, Van Leeuwen FE, Andersson M, Glimelius B, 

Joensuu T, Lynch CF, Curtis RE, Holowaty E, Storm H, Pukkala E, van't Veer 

MB, Fraumeni JF, Boice JD, Jr., Clarke EA, Travis LB. Lung cancer after 

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NRC. Health effects of exposure to radon: BEIR VI. Washington, DC: National 

Academy Press; 1999. 
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NRC Health Risks from Exposure to Low Levels of Ionizing Radiation – BEIR VII 

(Phase 2). A report of the Board on Radiation Effects Research. The National 

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