Abstract
The overlap coefficient ((Formula presented.)) 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 (Formula presented.) estimator using linear regression with a possible Box-Cox transformation. Bootstrap-based confidence intervals for the covariate-specific (Formula presented.) 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.
| Original language | English |
|---|---|
| Journal | Journal of Biopharmaceutical Statistics |
| Early online date | 25 Aug 2025 |
| DOIs | |
| State | E-pub ahead of print - 25 Aug 2025 |
Bibliographical note
Publisher Copyright:© 2025 Taylor & Francis Group, LLC.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Bootstrap
- ROC curves
- box-cox transformation
- diabetes melitus
- regression modeling
ASJC Scopus subject areas
- Statistics and Probability
- Pharmacology
- Pharmacology (medical)
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