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

Nature of this Talk • The data results here are in the paper of the same title in 511-20 Radiat. Res.

(2003) • After summarizing these I will turn to more general statistical issues • These slides and the paper can be obtained at http://home.att.ne.jp/apple/pierce 2

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

Radiation None Moderate High Smoking None Moderate Heavy 1 10 20 1.25

10.25

12.5

20.25

25 1.5

10.5

15 20.5

30

3

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 4

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 5

Dose Distributions 30000 20000 10000 Men Women 0 0 1-15 16-25

Cigarettes per Day

>25 16000 12000 8000 4000 0 <0.005

0.005-0.5

0.5-1.0

Lung Radiation Dose (Sv)

>1.0

Men Women 6

Smokers by Radiation Dose Analysis does not assume independence of smoking and radiation dose, but this is of some interest 100 80 60 40 20 0 Hiro Men Naga Men Hiro Wom Naga Wom 0 0 - 0.5

0.5 - 1

Radiation Dose (Sv)

>1 7

Simplest Analysis: Radiation Relative Risk within Smoking Levels 2 1.6

1.2

0.8

0.4

0 0 1-15 16-25

Smoking Level (cigarettes/day)

>25 If effects were multiplicative these ERRs would be equal: P-value = 0.02 for testing this 8

More Complete Analysis: Radiation Risk Relative to Non-Smoker Baseline Rates 3 2 1 0 6 5 4 Unconstrained w / Error Bars Fitted Additive Model Fitted Multiplicative Model 0 >25 1-15 16-25

Sm oking Level (cigarettes/day)

P-value for testing additive effects 0.20

9

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 10

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 11

Analysis Modeling • For simplest analysis, though, need no explicit model for smoking and can use for lung cancer  

s d g

} a, b, s : categories of age, birth cohort, smoking level g : gender d : dose continuous 12

Joint Effects Modeling • Take model for smoking effect as   {1    

b

} • Then, combining, the main model is   {1    

b

 (    } 13

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 14

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 15

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 16

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

Radiation None Moderate High Risk Factor None Moderate Heavy 1 1.25

1.5

1.25

1.5

1.56

1.75

1.88

1.5

1.75

1.88

2 2.25

17

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 18

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 19

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 20

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 21

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