Skaltsa Konstantina, Jover Lluís, Carrasco Josep Lluís
Public Health Department, University of Barcelona, Casanova, Spain.
Biom J. 2010 Oct;52(5):676-97. doi: 10.1002/bimj.200900294.
Medical diagnostic tests are used to classify subjects as non-diseased or diseased. The classification rule usually consists of classifying subjects using the values of a continuous marker that is dichotomised by means of a threshold. Here, the optimum threshold estimate is found by minimising a cost function that accounts for both decision costs and sampling uncertainty. The cost function is optimised either analytically in a normal distribution setting or empirically in a free-distribution setting when the underlying probability distributions of diseased and non-diseased subjects are unknown. Inference of the threshold estimates is based on approximate analytically standard errors and bootstrap-based approaches. The performance of the proposed methodology is assessed by means of a simulation study, and the sample size required for a given confidence interval precision and sample size ratio is also calculated. Finally, a case example based on previously published data concerning the diagnosis of Alzheimer's patients is provided in order to illustrate the procedure.
医学诊断测试用于将受试者分类为未患病或患病。分类规则通常包括使用通过阈值进行二分的连续标志物的值对受试者进行分类。在此，通过最小化一个考虑决策成本和抽样不确定性的成本函数来找到最佳阈值估计。当患病和未患病受试者的潜在概率分布未知时，成本函数在正态分布设置中通过解析方法进行优化，或在自由分布设置中通过经验方法进行优化。阈值估计的推断基于近似解析标准误差和基于自助法的方法。通过模拟研究评估所提出方法的性能，并计算给定置信区间精度和样本量比所需的样本量。最后，提供了一个基于先前发表的有关阿尔茨海默病患者诊断数据的案例，以说明该程序。
