A Regression Model for Risk Difference Estimation in Population-Based Case-Control Studies Clarifies Gender Differences in Lung Cancer Risk of Smokers and Never Smokers

Published In: BMC Medical Research Methodology, v. 13:143, 2013

Posted on RAND.org on January 01, 2013

by Stephanie Ann Kovalchik, Sara de Matteis, Maria Teresa Landi, Neil E. Caporaso, Ravi Varadhan, Dario Consonni, Andrew W. Bergen, Hormuzd A. Katki, Sholom Wacholder

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BACKGROUND: Additive 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. METHODS: Using 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. RESULTS: In 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. CONCLUSIONS: In 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.

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