Ruddat Inga, Kadlec Kristina, Schwarz Stefan, Kreienbrock Lothar
Berl Munch Tierarztl Wochenschr. 2014 Sep-Oct;127(9-10):349-58.
This paper summarizes statistical methods to describe susceptibility data. A frequent data basis in resistance studies are minimum inhibitory concentrations (MICs), measured for different antimicrobial agents. In the statistical context these (semi) quantitative MIC values are ordinal scaled. Therefore, they should be analysed with statistical tools appropriate for ordinal data. The resistance situation for each antimicrobial agent is often described using frequency distributions of MIC values. Resistance patterns can be described by frequencies of resistance profiles. More detailed insights into appearance and changes of simultaneous resistance against different agents are provided by a systematic analysis of dependency structure in susceptibility data. Furthermore, the calculation of differences between resistance profiles using appropriate distance measures enables the application of common methods of multivariate statistic for description and more complex analysis of susceptibility data. To improve the comparability of study results, it is desirable to present as much information as possible in a uniform way.
本文总结了描述药敏数据的统计方法。耐药性研究中常用的数据基础是针对不同抗菌药物测得的最低抑菌浓度（MIC）。在统计背景下，这些（半）定量的MIC值是按顺序标度的。因此，应使用适用于顺序数据的统计工具进行分析。每种抗菌药物的耐药情况通常用MIC值的频率分布来描述。耐药模式可用耐药谱的频率来描述。通过对药敏数据中的依赖结构进行系统分析，可以更深入地了解对不同药物同时耐药的出现情况和变化。此外，使用适当的距离度量来计算耐药谱之间的差异，能够应用多元统计的常用方法来描述和更复杂地分析药敏数据。为了提高研究结果的可比性，希望以统一的方式呈现尽可能多的信息。
