Abstract

In prognostic/diagnostic medical research, it is often the goal to identify a biomarker that differentiates between patients with and without a condition, or patients that will have good or poor response to a given treatment. The statistical literature is abundant with methods for evaluating single biomarkers for these purposes. However, in practice, a single biomarker rarely captures all aspects of a disease process; therefore, it is often the case that using a combination of biomarkers will improve discriminatory ability. A variety of methods have been developed for combining biomarkers based on the maximization of some global measure or cost-function. These methods usually create a score based on a linear combination of the biomarkers, upon which the standard single biomarker methodologies (such as the Youden’s index) are applied. However, these single biomarker methodologies do not account for the multivariable nature of the combined biomarker score. In this article we present generalized inference and bootstrap approaches to estimating confidence intervals for the Youden’s index and corresponding cut-point for a combined biomarker. These methods account for inherent dependencies and provide accurate and efficient estimates. A simulation study and real-world example utilize data from a Duchene Muscular Dystrophy study are also presented.

摘要

在预后/诊断医学研究中，通常的目标是确定一种生物标志物，以区分有无疾病的患者，或对给定治疗反应良好或不良的患者。统计文献中有大量用于评估用于这些目的的单一生物标志物的方法。然而，在实践中，单一的生物标志物很少能捕捉到疾病过程的所有方面；因此，通常情况下，使用生物标志物的组合会提高辨别能力。已经开发了多种方法用于基于某些全局测量或成本函数的最大化来组合生物标志物。这些方法通常根据生物标志物的线性组合创建分数，并在其上应用标准的单一生物标志物方法（例如 Youden 指数）。然而，这些单一的生物标志物方法并没有考虑组合生物标志物评分的多变量性质。在这篇文章中，我们提出了广义推理和引导程序方法来估计 Youden 指数的置信区间和组合生物标志物的相应分界点。这些方法考虑了固有的依赖性并提供准确有效的估计。还介绍了一个模拟研究和真实世界的例子，利用来自杜氏肌营养不良症研究的数据。这些方法考虑了固有的依赖性并提供准确有效的估计。还介绍了一个模拟研究和真实世界的例子，利用来自杜氏肌营养不良症研究的数据。这些方法考虑了固有的依赖性并提供准确有效的估计。还介绍了一个模拟研究和真实世界的例子，利用来自杜氏肌营养不良症研究的数据。