Department of Statistics, TU Dortmund University, Dortmund, 44221, Germany.
Department of R&D Global Biostatistics, Epidemiology and Medical Writing, Merck KGaA, Darmstadt, 64293, GermanyPresent address.
Bioinformatics. 2021 Aug 4;37(14):1990–1996. doi: 10.1093/bioinformatics/btab043. Epub 2021 Jan 30.
An important goal of concentration-response studies in toxicology is to determine an 'alert' concentration where a critical level of the response variable is exceeded. In a classical observation-based approach, only measured concentrations are considered as potential alert concentrations. Alternatively, a parametric curve is fitted to the data that describes the relationship between concentration and response. For a prespecified effect level, both an absolute estimate of the alert concentration and an estimate of the lowest concentration where the effect level is exceeded significantly are of interest.
In a simulation study for gene expression data, we compared the observation-based and the model-based approach for both absolute and significant exceedance of the prespecified effect level. Results show that, compared to the observation-based approach, the model-based approach overestimates the true alert concentration less often and more frequently leads to a valid estimate, especially for genes with large variance.
The code used for the simulation studies is available via the GitHub repository: https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations.
Supplementary data are available at Bioinformatics online.
毒理学中浓度-反应研究的一个重要目标是确定一个“警报”浓度,超过该浓度时,反应变量的临界水平就会被超过。在经典的基于观察的方法中,只有测量的浓度被视为潜在的警报浓度。或者,可以拟合一条描述浓度与反应之间关系的参数曲线。对于指定的效应水平,感兴趣的是警报浓度的绝对估计值和显著超过指定效应水平的最低浓度的估计值。
在基因表达数据的模拟研究中,我们比较了基于观察的方法和基于模型的方法,这两种方法都考虑了绝对和显著超过指定效应水平的情况。结果表明,与基于观察的方法相比,基于模型的方法不太经常高估真实的警报浓度,并且更频繁地给出有效估计,特别是对于方差较大的基因。
模拟研究中使用的代码可通过 GitHub 存储库获得:https://github.com/FKappenberg/Paper-IdentificationAlertConcentrations。
补充数据可在 Bioinformatics 在线获得。