Equivalence Assessment via the Difference Between Two AUCs in a Matched-Pair Design with Nonignorable Missing Endpoints

Abstract

Equivalence assessment via various indices such as relative risk has been widely studied in a matched-pair design with discrete or continuous endpoints over the past years. But existing studies mainly focus on the fully observed or missing at random endpoints. Nonignorable missing endpoints are commonly encountered in a matched-pair design. To this end, this paper proposes several novel methods to assess equivalence of two diagnostics via the difference between two correlated areas under ROC curves (AUCs) in a matched-pair design with nonignorable missing endpoints. An exponential tilting model is utilized to specify the nonignorable missing endpoint mechanism. Three nonparametric approaches and three semiparametric approaches are developed to estimate the difference between two correlated AUCs based on the kernel-regression imputation, inverse probability weighted (IPW), and augmented IPW methods. Under some regularity conditions, we show the consistency and asymptotic normality of the proposed estimators. Simulation studies are conducted to study the performance of the proposed estimators. Empirical results show that the proposed methods outperform the complete-case method. An example from clinical studies is illustrated by the proposed methodologies.