Term of Award
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mathematical Sciences
Committee Member 1
Committee Member 2
In non-inferiority testing, the decision of whether a proposed treatment is non-inferior to a reference treatment depends on model assumptions and choices of acceptable tolerance limits. Here, we consider a method that employs kernels to estimate the probability density functions of both the experimental and reference populations from two independent samples. Based on these densities, we introduce a quantity called the overlap coefficient or overlap measure. A bootstrap technique is helpful in exploring the distribution and variance empirically. We derive the distribution of this measure and define a hypothesis test that can be applied to the non-inferiority setting under some simplifying assumptions about distributions of the populations.
Ward, Larie C., "Non-Inferiority Testing: Kernel Estimation and Overlap Measure" (2022). Electronic Theses and Dissertations. 2456.
Research Data and Supplementary Material
Biostatistics Commons, Clinical Trials Commons, Data Science Commons, Design of Experiments and Sample Surveys Commons