Shu Di, Zou Guangyong
Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
Stat Methods Med Res. 2023 Apr;32(4):748-759. doi: 10.1177/09622802231151210. Epub 2023 Feb 1.
Estimation of areas under receiver operating characteristic curves and their differences is a key task in diagnostic studies. Here we develop closed-form sample size formulas for such studies with a focus on estimation rather than hypothesis testing, by explicitly incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. For sample size estimation purposes, we introduce a normality-based variance function for valid estimation allowing for unequal variances of observations in the disease and non-disease groups. Simulation results demonstrate that the proposed formulas produce empirical assurance probability close to the pre-specified assurance probability and empirical coverage probability close to the nominal level. Compared with a frequently used existing variance function, the proposed function provides more accurate and efficient sample size estimates. For an illustration of the proposed formulas, we present real-world worked examples. To facilitate implementation, we have developed an online calculator openly available at https://dishu.page/calculator/.
估计接受者操作特征曲线下的面积及其差异是诊断研究中的一项关键任务。在此，我们针对此类研究开发了封闭式样本量公式，重点在于估计而非假设检验，通过明确纳入预先指定的精度和保证度，其中精度由置信区间的下限表示，保证度由达到该下限的概率表示。出于样本量估计的目的，我们引入了一个基于正态性的方差函数，用于有效估计，允许疾病组和非疾病组观察值的方差不相等。模拟结果表明，所提出的公式产生的经验保证概率接近预先指定的保证概率，经验覆盖概率接近名义水平。与常用的现有方差函数相比，所提出的函数提供了更准确、高效的样本量估计。为说明所提出的公式，我们给出了实际应用的示例。为便于实施，我们开发了一个在线计算器，可在https://dishu.page/calculator/上公开获取。
