Confidence interval estimation of the difference between two sensitivities to the early disease stage

Tuochuan Dong, Le Kang, Alan Hutson, Chengjie Xiong, Lili Tian

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Although most of the statistical methods for diagnostic studies focus on disease processes with binary disease status, many diseases can be naturally classified into three ordinal diagnostic categories, that is normal, early stage, and fully diseased. For such diseases, the volume under the ROC surface (VUS) is the most commonly used index of diagnostic accuracy. Because the early disease stage is most likely the optimal time window for therapeutic intervention, the sensitivity to the early diseased stage has been suggested as another diagnostic measure. For the purpose of comparing the diagnostic abilities on early disease detection between two markers, it is of interest to estimate the confidence interval of the difference between sensitivities to the early diseased stage. In this paper, we present both parametric and non-parametric methods for this purpose. An extensive simulation study is carried out for a variety of settings for the purpose of evaluating and comparing the performance of the proposed methods. A real example of Alzheimer's disease (AD) is analyzed using the proposed approaches.

Original languageEnglish
Pages (from-to)270-286
Number of pages17
JournalBiometrical Journal
Issue number2
StatePublished - Mar 2014


  • Box-Cox transformation
  • Diagnostic studies
  • Disease with three ordinal stages
  • Generalized inference
  • Sensitivity to the early diseased stage


Dive into the research topics of 'Confidence interval estimation of the difference between two sensitivities to the early disease stage'. Together they form a unique fingerprint.

Cite this