Searching for an optimal AUC estimation method: a never-ending task?
Searching for an optimal AUC estimation method: a never-ending task?
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DOI：
10.1007/s10928-014-9392-y
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发表时间：
2014-12-01
影响因子：
2.5
通讯作者：
Jawien, Wojciech
中科院分区：
文献类型：
--
作者：
Jawien, Wojciech
一个有效的方法，建设的线性估计的AUC在有限的时间间隔内，最佳的极大极小意义上，开发和证明五个PK模型。这些模型可以作为显式C（t）关系给出或由微分方程定义。对于高变异性和丰富的采样，最佳方法仅比最佳梯形或标准数值方法（高斯-勒让德或克伦肖-柯蒂斯求积）具有适度的优势。最优估计量和其他方法之间的差异随着样本量的减小或变异性的减小而变得更加明显。所描述的估计方法可能在开发用于AUC测定的有限采样策略中显得有用，作为广泛使用的基于回归的方法的替代方案。据指出，许多替代办法也是可能的。
An effective method of construction of a linear estimator of AUC in the finite interval, optimal in the minimax sense, is developed and demonstrated for five PK models. The models may be given as an explicit C(t) relationship or defined by differential equations. For high variability and rich sampling the optimal method is only moderately advantageous over optimal trapezoid or standard numerical approaches (Gauss-Legendre or Clenshaw-Curtis quadratures). The difference between the optimal estimator and other methods becomes more pronounced with a decrease in sample size or decrease in the variability. The described estimation method may appear useful in development of limited-sampling strategies for AUC determination, as an alternative to the widely used regression-based approach. It is indicated that many alternative approaches are also possible.