Mathematical Institute, Heinrich Heine University, Düsseldorf, Germany.
Department of Statistics, TU Dortmund University, Dortmund, Germany.
Biometrics. 2023 Sep;79(3):2076-2088. doi: 10.1111/biom.13799. Epub 2022 Dec 7.
The determination of alert concentrations, where a pre-specified threshold of the response variable is exceeded, is an important goal of concentration-response studies. The traditional approach is based on investigating the measured concentrations and attaining statistical significance of the alert concentration by using a multiple t-test procedure. In this paper, we propose a new model-based method to identify alert concentrations, based on fitting a concentration-response curve and constructing a simultaneous confidence band for the difference of the response of a concentration compared to the control. In order to obtain these confidence bands, we use a bootstrap approach which can be applied to any functional form of the concentration-response curve. This particularly offers the possibility to investigate also those situations where the concentration-response relationship is not monotone and, moreover, to detect alerts at concentrations which were not measured during the study, providing a highly flexible framework for the problem at hand.
确定警戒浓度,即超过响应变量的预定阈值,是浓度-反应研究的一个重要目标。传统的方法是基于调查测量浓度,并通过使用多重 t 检验程序达到警戒浓度的统计显著性。在本文中,我们提出了一种基于模型的新方法来识别警戒浓度,该方法基于拟合浓度-反应曲线,并构建一个用于比较浓度与对照的响应差异的同时置信带。为了获得这些置信带,我们使用了一种自举方法,该方法可应用于任何形式的浓度-反应曲线。这特别提供了研究浓度-反应关系不是单调的情况的可能性,而且,还可以在研究过程中未测量到的浓度处检测到警报,为手头的问题提供了一个高度灵活的框架。