Generalization of Net Benefit of Diagnostic Tests into Multi-stage Clinical Conditions: A Collapsing Approach
Term of Award
Doctor of Public Health in Biostatistics (Dr.P.H.)
Document Type and Release Option
Dissertation (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Biostatistics, Epidemiology, and Environmental Health Sciences
Committee Member 1
Committee Member 2
Evaluating diagnostic tests based on benefit-risk involves both the tests’ accuracy and the clinical implications of the diagnostic errors. Diagnostic tests are commonly classified into two stages: either positive or negative for a clinical condition (diseased or non-diseased). However, some diseases have more than two stages, such as Alzheimer’s. In diseases with more than two stages, the benefits and risks of the clinical consequences could differ from stage to stage. I could not find any investigations to account for the difference in benefits and risks of tests with more than two stages in the literature. The benefit to cost values for each stage of the disease could be different. This dissertation extends the net benefit approach of evaluating diagnostic tests in binary disease cases to multi-stage clinical conditions. Consequently, I extend the diagnostic yield table to multi-stage clinical conditions. I develop a decision process based on net benefit for evaluating diagnostic tests. The decision process provides additional interpretation for rule-in or rule-out clinical conditions and their adverse consequences from unnecessary workups in multi-stage diseases. Numerical examples, as well as real data, are provided to illustrate the proposed measures.
Ahmed, Ferdous, "Generalization of Net Benefit of Diagnostic Tests into Multi-stage Clinical Conditions: A Collapsing Approach" (2022). Electronic Theses and Dissertations. 2409.
Research Data and Supplementary Material