Multivariate probability-based detection of drug-induced hepatic signals.
Multivariate probability-based detection of drug-induced hepatic signals.
复制标题
DOI：
10.2165/00139709-200625010-00003
复制
发表时间：
2006-01-01
期刊：
影响因子：
--
通讯作者：
Trost, Donald C
中科院分区：
文献类型：
--
作者：
Trost, Donald C
药物诱导肝脏效应的临床信号检测是一门非常不精确的科学。普通临床实验室检查是肝脏变化的主要生物标志物。临床医生已经开发了启发式规则来诊断肝脏疾病并监测这些变化。这些都是基于实验室的参考限值，这在很大程度上也是启发式的。本文回顾了单变量参考限的一些统计特性，并说明了它们如何能够并且应该扩展到多变量参考区域。例如，在单变量方法中，不能指定假阳性的概率，并且假阳性的概率随着评估的分析物数量的增加而增加。然而，准确的参考区域需要来自参考群体的非常大的样本。尽管相对于最大似然估计器，均匀最小方差无偏估计器可以极大地提高均方误差效率，但它仍然需要数万个参考样品来估计20种分析物的95%参考区域，例如95 +/-1%的数量级。提供了用于构造椭圆参考区域估计量和用于样本大小确定的方法。小型实验室进行这些计算是不可行的，除非可以实施更严格的标准化方法，并将各机构的数据合并。如果实施了使实验室间结果具有可比性的技术，则具有电子医疗记录的大型医疗保健系统和大型制药公司单独或合作可以产生足够的样本量用于准确的参考区域。退出参考区域，无论是基于人群还是个体化，都只能告诉您患者何时从稳定状态发生变化。患者结果进入的区域和这种变化的动态可能包含相当多的生物信息。一个例子是Hy法则。随着新的昂贵的生物标志物数量的增加，找到更好的方法来使用我们已经收集的数据，使用新的生物标志物进行验证，可能更具成本效益。数学和计算机可以帮助我们。
Clinical signal detection of drug-induced hepatic effects is a very inexact science. Ordinary clinical laboratory tests are the primary biomarkers for liver changes. Heuristic rules have been developed by clinicians for diagnosing liver disease and monitoring these changes. These are based on laboratory reference limits, which are also largely heuristic. This article reviews some of the statistical characteristics of univariate reference limits and shows how they can and should be extended to multivariate reference regions. For instance, in the univariate approach, the probability of a false positive cannot be specified and grows with increasing numbers of analytes evaluated. However, accurate reference regions require very large samples from reference populations. Although the uniformly minimum variance unbiased estimator can greatly improve the mean-squared-error efficiency relative to a maximum likelihood estimator, it still requires tens of thousands of reference samples to estimate the 95% reference region for 20 analytes to an order of 95 +/- 1%, for example. Methods for constructing the elliptical reference region estimators and for sample size determination are provided. It is not feasible for small laboratories to make these calculations unless more rigorous methods of standardisation can be imposed and data merged across institutions. Large healthcare systems with electronic medical records and large pharmaceutical companies singly or in collaboration could generate sufficient sample sizes for accurate reference regions if techniques to make inter-laboratory results comparable are implemented. Exiting a reference region, whether population-based or individualised, can only tell you when the patient has changed from steady state. The region into which the patient's results enter and dynamics of this change are likely to contain considerable biological information. An example of this is Hy's rule. As the number of new, expensive biomarkers grows, it may be more cost-effective to find better ways to use the data we already collect, using the new biomarkers for validation. Mathematics and computers can help do this.