Garreau M, Joly H, Poirier M F, Deniker P
Sem Hop. 1982 Apr 8;58(14):833-9.
We describe a statistical technique for achieving unbiased classification of 111 psychopharmaceutical agents from computerized clinical data. Three different methods were used: the reciprocal averaging method describes and pictures the clusterings of the numerous clinical datum available; the cluster analysis method corroborates the arrangement of the drugs and their effects into groups; the discriminating analysis method assigns the remaining drugs to their groups. The drugs were ultimately classified into six different groups. One of these groups, which included many of the antidepressant drugs, was reanalyzed with the reciprocal averaging method. This led to separating the monoamine oxidase inhibitors from the other antidepressants. Our technique will be immediately useful for classifying new psychopharmaceutical agents. If biologic data are included, this technique will be helpful for studying correlations between the structure and the effects of drugs.
我们描述了一种用于从计算机化临床数据中对111种精神药物进行无偏分类的统计技术。使用了三种不同的方法：倒数平均法描述并描绘了众多可用临床数据的聚类情况；聚类分析法证实了药物及其效应的分组排列；判别分析法将其余药物归入相应组。这些药物最终被分为六个不同的组。其中一组包括许多抗抑郁药物，用倒数平均法对其进行了重新分析。这导致将单胺氧化酶抑制剂与其他抗抑郁药物区分开来。我们的技术将立即用于对新的精神药物进行分类。如果纳入生物学数据，该技术将有助于研究药物结构与效应之间的相关性。
