Detection and prediction limits for identifying highly confusable drug names from experimental data.
Detection and prediction limits for identifying highly confusable drug names from experimental data.
复制标题
从实验数据中识别高度易混淆的药物名称的检测和预测极限。
DOI：
10.1080/10543406.2015.1052481
复制
发表时间：
2016
影响因子：
1.1
通讯作者：
Bhaumik,DulalK
中科院分区：
文献类型：
--
作者：
Lambert,BruceL;Bhaumik,Runa;Zhao,Weihan;Bhaumik,DulalK
外观和发音相似的药物名称之间的混淆是常见的、昂贵的、有害的，而且很难预防。一种预防策略是在批准之前筛选拟议的新药名称是否容易混淆。由于缺乏实验设计和统计方法来支持关于拟议的新名称是否具有不可接受的混淆程度的有效推断，对易混淆的预批准测试的广泛接受受到了影响。识别混淆名称的一种方法是对一组药物名称进行记忆和感知实验，其中包括新名称和一组对照名称(例如，市场上已有的名称)。这项实验将为每个名字产生一个观察到的错误率。可以通过将新名称的错误率与对照名称的错误率的分布进行比较来作出关于新名称的可接受性的推断。我们描述了四个关于药物名称的记忆和感知实验，这些实验以临床医生为参与者。每个实验都包括被指定为试验名称和对照名称的药物名称。我们演示了如何使用Logistic回归、泊松预测极限和高度可靠的可信区间的组合来识别和应用阈值来识别不可接受的混淆名称。我们的模型显示出与数据很好的吻合。这些实验设计和分析方法在拟议的新药名称的审批前测试中以及在类似的监管方案中应该有用，在这些方案中，有必要对新产品与旧产品的相对安全性或有效性做出推断。
Confusions between drug names that look and sound alike are common, costly, harmful, and difficult to prevent. One prevention strategy is to screen proposed new drug names for confusability before approving them. Widespread acceptance of preapproval tests of confusability is compromised by the lack of experimental designs and statistical methods to support valid inferences about whether a proposed new name is unacceptably confusing. One way of identifying confusing names is to conduct memory and perception experiments on a set of drug names which would include both the new name and a set of control names (e.g., names already on the market). The experiment would yield an observed error rate for every name. Inferences about the acceptability of the new name can be made by comparing the error rate of the new name to the distribution of error rates of the control names. We describe four memory and perception experiments on drug names, carried out using clinicians as participants. Each experiment included drug names designated as test and control names. We demonstrate how to use a combination of logistic regression, Poisson prediction limits, and highly assured credible intervals to identify and apply a threshold for identifying unacceptably confusing names. Our models show an excellent fit to the data. These experimental designs and analytic methods should be useful in the preapproval testing of proposed new drug names and in similar regulatory scenarios where it is necessary to draw inferences about the comparative safety or effectiveness of new vs. old products.