Evaluation of various statistical methods to describe accurately associations between exposures and disease are constantly being explored. Spline regression has been suggested as an alternative to using categorized variables in studies of disease etiology, as it uses all data points to estimate the shape of the association between a given exposure and disease outcome. It has been proposed that this method is especially beneficial when associations are concentrated in a small range of the overall distribution of the exposure. In this study, we use data from a large case-control study of colon cancer to evaluate associations obtained from logistic regression models that use spline regression for main exposure and confounder effects with those that use categorized variables for main exposure. Our results show that for variables for which the association appears to be linear, such as body size and dietary intake of calcium, fiber, and cholesterol, associations are similar when estimates are generated from spline or categorized variable models. For other variables, such as total energy intake, for which associations appear to be strongest in the upper end of the distribution, estimates of association appear to be conservative when using categorized variables. The data also suggest that selection of cut points for the categorized variables may have an impact on the associations observed. Spline regression appears to be useful to estimate the shape of the association between a given exposure and disease and may provide guidance as to the appropriateness of using categorized variables. The risk estimates from spline regression appear to be similar to those from traditional categorical methods. When effects are large or rapidly changing, spline models may more appropriately describe the association.