Modeling lung cancer risk in case-control studies using a new dose metric of smoking.

Many approaches have been taken to adjust for smoking in modeling cancer risk. In case-control studies, these metrics are often used arbitrarily rather than being based on the properties of the metric in the context of the study. Depending on the underlying study design, hypotheses, and base population, different metrics may be deemed most appropriate. We present our approach to evaluating different smoking metrics. We examine the properties of a new metric, "logcig-years", that we initially derived from using a biological model of DNA adduct formation. We compare this metric to three other smoking metrics, namely pack-years, square-root pack-years, and a model in which smoking duration and intensity are separate variables. Our comparisons use generalized additive models and logistic regression to examine the relationship between the logit probability of cancer and each of the metrics, adjusting for other covariates. All models were fit using data from a lung cancer study of 1,275 cases and 1,269 controls that has focused on gene-smoking relationships. There was a very significant, linear relationship between logcig-years and the logit probability of lung cancer in this sample, without any need to adjust for smoking status. These properties together were not shared by the other metrics. In this sample, logcig-years captured more information about smoking that is important in lung cancer risk than the other metrics. In conclusion, we provide a general framework for evaluating different smoking metrics in studies where smoking is a critical variable.