在具有不可忽略的缺失终点的配对设计中,通过两个 AUC 之间的差值进行等效性评估

IF 1.1 4区 数学 Q1 MATHEMATICS Communications in Mathematics and Statistics Pub Date : 2024-07-17 DOI:10.1007/s40304-023-00393-z
Yunqi Zhang, Weili Cheng, Puying Zhao
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引用次数: 0

摘要

在过去几年中,通过各种指数(如相对风险)进行的等效性评估已在离散或连续终点的配对设计中得到广泛研究。但现有的研究主要集中于完全观察到的终点或随机缺失的终点。在配对设计中,常会遇到不可忽略的缺失终点。为此,本文提出了几种新的方法,通过两个相关的 ROC 曲线下面积(AUC)之间的差值来评估两个诊断的等效性。利用指数倾斜模型来说明不可忽略的终点缺失机制。基于核回归估算、反概率加权(IPW)和增强 IPW 方法,我们开发了三种非参数方法和三种半参数方法来估算两个相关 AUC 之间的差异。在一些正则条件下,我们证明了所提出的估计值的一致性和渐近正态性。我们还进行了模拟研究,以考察所提估计方法的性能。实证结果表明,所提出的方法优于完全情况方法。通过临床研究中的一个例子来说明所提出的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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.

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来源期刊
Communications in Mathematics and Statistics
Communications in Mathematics and Statistics Mathematics-Statistics and Probability
CiteScore
1.80
自引率
0.00%
发文量
36
期刊介绍: Communications in Mathematics and Statistics is an international journal published by Springer-Verlag in collaboration with the School of Mathematical Sciences, University of Science and Technology of China (USTC). The journal will be committed to publish high level original peer reviewed research papers in various areas of mathematical sciences, including pure mathematics, applied mathematics, computational mathematics, and probability and statistics. Typically one volume is published each year, and each volume consists of four issues.
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