McNamee Roseanne
Biostatistics Group, School of Epidemiology and Health Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK.
Stat Med. 2002 Dec 15;21(23):3609-25. doi: 10.1002/sim.1318.
The cost efficiency of estimation of sensitivity, specificity and positive predictive value from two-stage sampling designs is considered, assuming a relatively cheap test classifies first-stage subjects into several categories and an expensive gold standard is applied at stage two. Simple variance formulae are derived and used to find optimal designs for a given cost ratio. The utility of two-stage designs is measured by the reduction in variances compared with one-stage simple random designs. Separate second-stage design is also compared with proportional allocation (PA). The maximum percentage reductions in variance from two-stage designs for sensitivity, specificity and positive predictive value estimation are P per cent, (1-P) per cent and W, respectively, where P is the population prevalence of disease and W the population percentage of test negatives. The optimum allocation of stage-two resources is not obvious: the optimum proportion of true cases at stage two may even be less than under PA. PA is near optimal for sensitivity estimation in most cases when prevalence is low, but inefficient compared with the optimal scheme for specificity.
考虑了从两阶段抽样设计估计灵敏度、特异度和阳性预测值的成本效率，假设一种相对便宜的检测方法将第一阶段的受试者分类为几个类别，而在第二阶段应用昂贵的金标准。推导了简单的方差公式，并用于找到给定成本比下的最优设计。两阶段设计的效用通过与单阶段简单随机设计相比方差的减少来衡量。还将单独的第二阶段设计与比例分配（PA）进行了比较。两阶段设计在估计灵敏度、特异度和阳性预测值时方差的最大百分比减少分别为P%、（1-P）%和W，其中P是疾病的总体患病率，W是检测阴性的总体百分比。第二阶段资源的最优分配并不明显：第二阶段真病例的最优比例甚至可能低于比例分配的情况。在患病率较低的大多数情况下，比例分配对于灵敏度估计接近最优，但与特异度的最优方案相比效率较低。
