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A quantitative method for measuring the relationship between an objective endpoint and patient reported outcome measures

机译:一种测量客观终点与患者报告的结局指标之间关系的定量方法

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摘要

Patient reported outcome measures (PROMs) become increasingly important for assessing the effectiveness of a drug or medical device. In order for a PROM to be claimed in labeling, the PROM has to be valid, reliable and able to detect a change if the targeted disease status changes. One approach to assess the quality of a patient reported outcome measure (PROM) is to investigate the association between the PROM and an objective clinical endpoint measuring the status of a disease/condition. However, methods assessing the association between continuous and discrete variables are limited, especially for correlated measurements. In this paper, we propose a method to assess such association with any type of samples with or without correlation. The method involves estimating the probability revealing the status of a subject’s disease/condition (called truth thereafter) through the subject’s reported outcomes. The probability is a conditional probability revealing truth given the relative location of the subject’s objective outcome compared to the subject-specific latent threshold in the objective endpoint. A consistent estimator for the probability is derived. The operating characteristics of the consistent estimator are illustrated using simulation. Our method is applied to hypothetical clinical trial data generated for an ophthalmic device as an illustration.
机译:患者报告的结局指标(PROM)对于评估药物或医疗设备的有效性变得越来越重要。为了在标签中声明PROM,PROM必须有效,可靠并且能够在目标疾病状态发生变化时检测到变化。评估患者报告结果测量(PROM)质量的一种方法是研究PROM与测量疾病/状况状态的客观临床终点之间的关联。但是,评估连续变量和离散变量之间的关联的方法非常有限,尤其是对于相关的测量。在本文中,我们提出了一种评估与任何类型的样本相关或不相关的关联的方法。该方法包括估算通过受试者报告的结果揭示受试者疾病/状况(此后称为真相)的概率。概率是一个条件概率,它揭示了给定受试者客观结果与受试者终点中特定于受试者的潜在阈值相比的相对位置的真相。得出概率的一致估计量。使用仿真来说明一致性估计器的操作特性。我们的方法应用于为眼科设备生成的假设临床试验数据作为说明。

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