Paterson Steve, Lello Joanne
Department of Biological Sciences, University of Stirling, Stirling FK9 4LA, UK.
Trends Parasitol. 2003 Aug;19(8):370-5. doi: 10.1016/s1471-4922(03)00149-1.
Statistical analysis of parasitological data provides a powerful method for understanding the biological processes underlying parasite infection. However, robust and reliable analysis of parasitological data from natural and experimental infections is often difficult where: (1) the distribution of parasites between hosts is aggregated; (2) multiple measurements are made on the same individual host in longitudinal studies; or (3) data are from 'noisy' natural systems. Mixed models, which allow multiple error terms, provide an excellent opportunity to overcome these problems, and their application to the analysis of various types of parasitological data are reviewed here.
寄生虫学数据的统计分析为理解寄生虫感染背后的生物学过程提供了一种强大的方法。然而，对来自自然感染和实验感染的寄生虫学数据进行稳健且可靠的分析往往很困难，具体情况如下：（1）宿主之间寄生虫的分布是聚集性的；（2）在纵向研究中对同一个体宿主进行多次测量；或者（3）数据来自“嘈杂”的自然系统。允许存在多个误差项的混合模型为克服这些问题提供了绝佳机会，本文对其在各类寄生虫学数据的分析中的应用进行了综述。
