Kon Kam King Guillaume, Veber Philippe, Charles Sandrine, Delignette-Muller Marie Laure
UMR CNRS 5558-Laboratoire de Biométrie et Biologie Évolutive, Unviersite Claude Bernard, Lyon, Villeurbanne, France.
Environ Toxicol Chem. 2014 Sep;33(9):2133-9. doi: 10.1002/etc.2644. Epub 2014 Aug 4.
Censored data are seldom taken into account in species sensitivity distribution (SSD) analysis. However, they are found in virtually every dataset and sometimes represent the better part of the data. Stringent recommendations on data quality often entail discarding a lot of these meaningful data, resulting in datasets of reduced size which lack representativeness of any realistic community. However, it is reasonably simple to include censored data in SSD by using an extension of the standard maximum likelihood method. The authors detail this approach based on the use of the R-package fitdistrplus, dedicated to the fit of parametric probability distributions. The authors present the new Web tool MOSAIC_SSD, that can fit an SSD on datasets containing any type of data, censored or not. The MOSAIC_SSD Web tool predicts any hazardous concentration and provides bootstrap confidence intervals on the predictions. Finally, the authors illustrate the added value of including censored data in SSD, taking examples from published data.
在物种敏感性分布(SSD)分析中,删失数据很少被考虑在内。然而,它们几乎在每个数据集中都能找到,有时还占数据的较大部分。对数据质量的严格建议往往需要丢弃大量这些有意义的数据,从而导致数据集规模减小,缺乏任何现实群落的代表性。然而,通过使用标准最大似然法的扩展,将删失数据纳入SSD相当简单。作者基于R包fitdistrplus(致力于参数概率分布拟合)详细介绍了这种方法。作者展示了新的网络工具MOSAIC_SSD,它可以对包含任何类型数据(无论是否删失)的数据集进行SSD拟合。MOSAIC_SSD网络工具可预测任何有害浓度,并为预测提供自助置信区间。最后,作者通过已发表数据的实例说明了在SSD中纳入删失数据的附加价值。