Beck J R
J Med Syst. 1980;4(2):237-52. doi: 10.1007/BF02222466.
The application of multivariate statistical technics to laboratory data analysis is a recent development in clinical pathology. The proliferation in number and type of diagnostic tests requires a simple method to extract predictors of clinical states from data generated by existing laboratory procedures, as well as a method to assess the usefulness of new tests as they are proposed. A series of algorithms can select a "best predictor" subset of laboratory tests and verify this reduced group's predictive diagnostic worth. The established predictive ability of existing laboratory tests can serve as a reference scale when a newly available laboratory test is evaluated. The proposed new test is included with the best predictors already identified and confirmed. In multivariate analysis the usefulness of the proposed new procedure in the predictive diagnosis scheme is determined.
将多元统计技术应用于实验室数据分析是临床病理学领域的一项最新进展。诊断测试在数量和类型上的激增,需要一种简单的方法从现有实验室程序产生的数据中提取临床状态的预测指标,同时也需要一种方法来评估新提出的测试的有用性。一系列算法可以选择实验室测试的“最佳预测指标”子集,并验证这个精简后的组的预测诊断价值。在评估新的实验室测试时,现有实验室测试已确立的预测能力可以作为一个参考标准。将新提出的测试与已经确定并确认的最佳预测指标放在一起。在多变量分析中,确定新提出的程序在预测诊断方案中的有用性。