Date of Award
Doctor of Philosophy (PhD)
Department of Mathematics and Statistics
Christine M. Schubert Kabban, PhD.
The current emphasis on including correlation when comparing diagnostic test performance is quite important, however, there are cases in which correlation effects may be negligible with respect to inference. This proposed work examines the impact of including correlation between classification systems with continuous features by comparing the optimal performance of two diagnostic tests with multiple outcomes as well as providing inference for a sequence of tests. We define the optimal point using Bayes Cost, a metric that sums the weighted misclassifications within a diagnostic test using a cost/benefit structure. Through simulation, we quantify the impact of correlation on standard errors comparing two tests and evaluate the resulting errors with respect to CI coverage and width under varying diagnostic test accuracy, sample size, cost/benefit structures, parametric assumptions and correlation levels. When formulas are required for better inference to include correlation, we provide updated computational techniques that properly extend the Delta and Generalized method. Additionally, to date, no methods have been applied to quantify the performance of a sequence of tests. Therefore, the inference methods derived in this work are extended to sequenced tests where feature correlation is unavoidable and must be accounted for when developing inference on tests.
DTIC Accession Number
Nunnally, Beau A., "Statistical Inference to Evaluate and Compare the Performance of Correlated Multi-State Classification Systems" (2018). Theses and Dissertations. 1914.