Work

Parametric and non‐parametric confidence intervals of the probability of identifying early disease stage given sensitivity to full disease and specificity with three ordinal diagnostic groups

Year: 2011

Type: article

Abstract: In practice, there exist many disease processes with three ordinal disease classes, that is, the non-diseased stage, the early disease stage, and the fully diseased stage. Because early disease stage is likely the best time window for treatment interventions, it is important to have diagnostic tests that have good diagnostic ability to discriminate the early disease stage from the other two stages. In this paper, we present both parametric and non-parametric approaches for confidence interval estimation of probability of detecting early disease stage given the true classification rates for non-diseased group and diseased group, namely, the specificity and the sensitivity to full disease. We analyze a data set on the clinical diagnosis of early-stage Alzheimer's disease from the neuropsychological database at the Washington University Alzheimer's Disease Research Center using the proposed approaches.

Source: Statistics in Medicine

DOI: 10.1002/sim.4401

Authors: Tuochuan Dong, Lili Tian, Alan Hutson, Chengjie Xiong

Institutions: University at Buffalo, State University of New York, Washington University in St. Louis

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Alzheimer's Association, National Institutes of Health, National Institute on Aging