A nested approach to evaluating dose-response and trend

Abstract

Purpose: Conventional dose-response and trend analysis fits either a linear or categorical logistic model and tests the resulting coefficients. These analyses, however, are based on implausible assumptions.

Methods: We present an alternative approach that uses likelihood ratio tests to compare nested regression models and determine when a model is rich enough to capture the data trends.

Results: For illustration, we apply this approach to data on diet and colorectal polyps.

Conclusions: Comparison of linear and quadratic spline logistic models indicates that the conventional approach of using only a linear logistic model would not appropriately describe the association between intake of fruits and vegetables and colorectal polyps in our data. Graphical checking further supports this conclusion.