Confidence intervals for the covariate-specific overlap coefficient (OVL)

Abstract

The overlap coefficient ($\mathrm{OVL}$) quantifies the similarity between two distributions through the overlapping area of their distribution functions. It has been discussed in the literature in a variety of different contexts. One approach for testing the bioequivalence of treatments is to measure the overlap of the distributions of individual responses to therapy. In some situations, covariates can significantly influence distributional overlap. This paper develops a covariate-specific $\mathrm{OVL}$ estimator using linear regression with a possible Box-Cox transformation. Bootstrap-based confidence intervals for the covariate-specific $\mathrm{OVL}$ are proposed and evaluated through extensive simulations. The methodology is illustrated using fingerstick post-prandial blood glucose measurements as a biomarker for diabetes patients adjusted for age.

Keywords: Bootstrap; ROC curves; box-cox transformation; diabetes melitus; regression modeling.