Examining the joint effect of multiple risk factors using exposure risk profiles: Lung cancer in nonsmokers
Papathomas M., Molitor J., Richardson S., Riboli E., Vineis P., Manuguerra M., Matullo G., Veglia F., Autrup H., Dunning AM., Garte S., Gormally E., Malaveille C., Guarrera S., Polidoro S., Saletta F., Peluso M., Airoldi L., Overvad K., Raaschou-Nielsen O., Clavel-Chapelon F., Linseisen J., Boeing H., Trichopoulos D., Kalandidi A., Palli D., Krogh V., Tumino R., Panico S., Bueno-De-Mesquita HB., Peeters PH., Lund E., Pera G., Martinez C., Amiano P., Barricarte A., Tormo MJ., Quiros JR., Berglund G., Janzon L., Jarvholm B., Day NE., Allen NE., Saracci R., Kaaks R., Ferrari P.
Background: Profile regression is a Bayesian statistical approach designed for investigating the joint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inference the exposure profiles of the subjects that is, the sequence of covariate values that correspond to each subject. objectives: We applied profile regression to a case-control study of lung cancer in nonsmokers, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, to estimate the combined effect of environmental carcinogens and to explore possible gene- environment interactions. Methods: We tailored and extended the profile regression approach to the analysis of case-control studies, allowing for the analysis of ordinal data and the computation of posterior odds ratios. We compared and contrasted our results with those obtained using standard logistic regression and classification tree methods, including multifactor dimensionality reduction. results: Profile regression strengthened previous observations in other study populations on the role of air pollutants, particularly particulate matter ≤ 10 μm in aerodynamic diameter (PM10), in lung cancer for nonsmokers. Covariates including living on a main road, exposure to PM10 and nitrogen dioxide, and carrying out manual work characterized high-risk subject profiles. Such combinations of risk factors were consistent with a priori expectations. In contrast, other methods gave less interpretable results. conclusions: We conclude that profile regression is a powerful tool for identifying risk profiles that express the joint effect of etiologically relevant variables in multifactorial diseases.