Empirical Likelihood-Based Confidence Intervals for the Sensitivity of a Continuous-Scale Diagnostic Test with Missing Data

Abstract

In a continuous-scale diagnostic test, the receiver operating characteristic (ROC) curve is useful to evaluate the range of the sensitivity at the cut-off point that yields a desired specificity. Many current studies on inference of the ROC curve focus on the complete data case. In this paper, an imputation-based profile empirical likelihood ratio for the sensitivity, which is free of bandwidth selection, is defined and shown to follow an asymptotically scaled Chi-square distribution. Two new confidence intervals are proposed for the sensitivity with missing data. Simulation studies are conducted to evaluate the finite sample performance of the proposed intervals in terms of coverage probability. A real example is used to illustrate the new methods.