Objectives: Estimation of areas under receiver operating characteristic curves (AUCs) and their differences is a key task in diagnostic studies. We aimed to derive, evaluate, and implement simple sample size formulas for such studies with a focus on estimation rather than hypothesis testing. Materials and Methods: Sample size formulas were developed 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. A new variance function was proposed for valid estimation allowing for unequal variances of observations in the disease and non-disease groups. Performance of the proposed formulas was evaluated through simulation. Results: Closed-form sample size formulas were obtained. Simulation results demonstrated that the proposed formulas produced empirical assurance probability close to the pre-specified assurance probability and empirical coverage probability close to the nominal 95%. Real-world worked examples were presented for illustration. Conclusions: Sample size formulas based on estimation of AUCs and their differences were developed. Simulation results suggested good performance in terms of achieving pre-specified precision and assurance probability. An online calculator for implementing the proposed formulas is openly available at https://dishu.page/calculator/.
翻译:目标:在接受者运行特征曲线(AUCs)下地区及其差异的估算是诊断性研究的一项关键任务,我们旨在为此类研究得出、评价和实施简单的样本规模公式,重点是估计而不是假设测试。材料和方法:样本规模公式的制定明确纳入了事先确定的精确度和保证度,精确的用信任区间和保证的下限的下限表示,以达到这一下限的概率为标志。提出了新的差异函数,以有效估算,允许疾病和非疾病组之间观测结果的不平等差异。通过模拟评估了拟议公式的性能。结果:获得了封闭式样本规模公式;模拟结果表明,拟议公式产生的实证保证概率接近预定保证概率和接近标准值95%。提供了真实世界工作的实例,以说明这些实例。结论:根据对AUCs的估计及其差异制定了样本规模公式。模拟结果表明,在达到预定前的精确度和保证概率方面表现良好。执行拟议公式的在线计算器。在 http://culpal/culpagement上公开提供。