Abstract
BackgroundAdditive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. Yet technical challenges have limited the consideration of additive risk models in case–control studies.MethodsUsing a flexible risk regression model that allows additive and multiplicative components to estimate absolute risks and risk differences, we report a new analysis of data from the population-based case–control Environment And Genetics in Lung cancer Etiology study, conducted in Northern Italy between 2002–2005. The analysis provides estimates of the gender-specific absolute risk (cumulative risk) for non-smoking- and smoking-associated lung cancer, adjusted for demographic, occupational, and smoking history variables.ResultsIn the multiple-variable lexpit regression, the adjusted 3-year absolute risk of lung cancer in never smokers was 4.6 per 100,000 persons higher in women than men. However, the absolute increase in 3-year risk of lung cancer for every 10 additional pack-years smoked was less for women than men, 13.6 versus 52.9 per 100,000 persons.ConclusionsIn a Northern Italian population, the absolute risk of lung cancer among never smokers is higher in women than men but among smokers is lower in women than men. Lexpit regression is a novel approach to additive-multiplicative risk modeling that can contribute to clearer interpretation of population-based case–control studies.
Highlights
Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk
There are several epidemiological questions that are more addressed with an additive risk model, where exposure effects are modeled on the absolute risk scale
We recently encountered the challenge of additive risk modeling with case–control data in an investigation of gender differences in smoking-associated lung cancer in the Environment and Genetics in Lung cancer Etiology (EAGLE) Study—a population-based case–control study conducted in Northern Italy between 2002–2005 [10]
Summary
Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. The multiplicative model quantifies the joint effects of exposures on the relative risk of disease and is the mainstay of case–control analysis [1]. We recently encountered the challenge of additive risk modeling with case–control data in an investigation of gender differences in smoking-associated lung cancer in the Environment and Genetics in Lung cancer Etiology (EAGLE) Study—a population-based case–control study conducted in Northern Italy between 2002–2005 [10]. In a logistic regression analysis of never and ever smokers of the EAGLE Study, De Matteis and colleagues found evidence of an interaction between gender and packyears smoked that suggested a higher susceptibility to lung cancer in men [11,12]. The authors sought to quantify the public health implications of the gender differences they found by estimating absolute risk differences of lung cancer in men and women, adjusted for other confounders. The risk difference estimates could theoretically be obtained with an additive risk model yet, unlike methods for multiplicative modeling, reliable methods for additive risk regression with case–control data were not available
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