Joint Effects of Radiation and Smoking on Lung Cancer Risk among Atomic Bomb Survivors Donald A. Pierce, RERF Gerald B. Sharp, RERF & NIAID Kiyohiko Mabuchi, NCI

2 Nature of this Talk The data results here are in the paper of the same title in Radiat. Res. (2003) After summarizing these I will turn to more general statistical issues These slides and the paper can be obtained at

3 Hypothetical RRs for Joint Effects Upper values for additive and lower for multiplicative

4 Nutshell Background Previous LSS analyses could not distinguish between multiplicative and additive effects –Main reason was that apparent smoking risks were quite small –Probably due to scarcity of cigarettes during and soon after the war In my view BEIR IV,VI results for miners & radon were equivocal in this respect

5 Smoking Information & Usage From mail and clinical surveys: 52,000 persons presenting 600 lung cancers Used only as levels: 0,1-15,16-25,>25 cigarettes/day, averaging over multiple responses Could estimate pack-years measure, but we think smoking rate may be preferable

6 Dose Distributions

7 Smokers by Radiation Dose Analysis does not assume independence of smoking and radiation dose, but this is of some interest

8 Simplest Analysis: Radiation Relative Risk within Smoking Levels If effects were multiplicative these ERRs would be equal: P-value = 0.02 for testing this

9 More Complete Analysis: Radiation Risk Relative to Non-Smoker Baseline Rates P-value for testing additive effects 0.20

10 Effects of Adjusting for Smoking Spuriously large female:male sex ratio in ERR is reduced to usual level Exposure-age effect contrary to usual direction is eliminated Entire pattern of ERR/Sv becomes similar to solid cancers in general For baseline rates female:male ratio is increased by factor 3-4 by adjustment

11 Statistical Modeling As for the basic LSS, and BEIR IV, developing suitable statistical models was challenging Is necessary to allow all RRs to depend on attained age, birth cohort, gender as well as radiation and smoking

12 Analysis Modeling For simplest analysis, though, need no explicit model for smoking and can use for lung cancer a, b, s : categories of age, birth cohort, smoking level g : gender d : dose continuous

13 Joint Effects Modeling Take model for smoking effect as Then, combining, the main model is

14 Remainder of Talk For the rest of the talk I will discuss issues arising in this work that are of more general interest In part, aim to be mildly provocative

15 Something about Confounding It was seen that smoking level and radiation dose are not related There is however confounding for the lung cancer sex-by-radiation interaction The radiation ERR/Sv is not a number but a pattern depending on … Thus smoking level and radiation effect are confounded

16 Prospects for Other Joint Effects Studies Hopeless to distinguish between multiplicative and additive effects unless –Other risk factor has an RR of 5 or so –Or focus is where the radiation risk is larger than usual More ambitious goals are even less attainable

17 Hypothetical RRs for Joint Effects If Other Factor Has Modest Effect Upper values for additive and lower for multiplicative

18 Use of Smoking Information Even if more than rough smoking rate were available, it would be difficult to use the information Must model the effect of smoking history for information to be useful Age of cessation is both unreliable and difficult to model Age of starting is similarly difficult to use

19 Use of Pack-Years Plausibly, the smoking RR for given rate is fairly constant in age, and then the RR for given pack-yrs will decrease with age Mutation modeling suggests the RR covariable pack-yrs/age, that is the lifetime average rate up to age-at-risk

20 Special Problem for Our Cohort Cigarettes were scarce during and soon after the war Causes a birth cohort effect in the smoking ERR, in terms of our smoking rates Again, pack-yrs/age might be a better covariable than either pack-yrs or smoking rate

21 Effect of Smoking on Radiation Exposure-Age Effect Baseline lung cancer rates increased strongly over most of our follow-up --- due to smoking Since effects are additive, this causes the radiation ERR exposure-age effect to increase with exposure age This is opposite to most cancers, but “corrected” by adjusting for smoking

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