Department of Public Health Sciences, Pennsylvania State University, Hershey, United States.
Department of Lymphoma and Myeloma, University of Texas MD Anderson Cancer Center, Houston, United States.
Elife. 2022 Aug 3;11:e78634. doi: 10.7554/eLife.78634.
The median-effect equation has been widely used to describe the dose-response relationship and identify compounds that activate or inhibit specific disease targets in contemporary drug discovery. However, the experimental data often contain extreme responses, which may significantly impair the estimation accuracy and impede valid quantitative assessment in the standard estimation procedure. To improve the quantitative estimation of the dose-response relationship, we introduce a novel approach based on robust beta regression. Substantive simulation studies under various scenarios demonstrate solid evidence that the proposed approach consistently provides robust estimation for the median-effect equation, particularly when there are extreme outcome observations. Moreover, simulation studies illustrate that the proposed approach also provides a narrower confidence interval, suggesting a higher power in statistical testing. Finally, to efficiently and conveniently perform common lab data analyses, we develop a freely accessible web-based analytic tool to facilitate the quantitative implementation of the proposed approach for the scientific community.
中效方程已被广泛用于描述剂量反应关系，并识别出在当代药物发现中激活或抑制特定疾病靶点的化合物。然而，实验数据通常包含极端反应，这可能会显著降低标准估计过程中的估计准确性，并阻碍有效的定量评估。为了提高剂量反应关系的定量估计，我们引入了一种基于稳健贝塔回归的新方法。在各种情况下的实质性模拟研究证明，该方法为中效方程提供了一致的稳健估计，特别是当存在极端结果观察值时。此外，模拟研究表明，该方法还提供了更窄的置信区间，表明在统计检验中具有更高的功效。最后，为了高效、方便地进行常见的实验室数据分析，我们开发了一个免费的基于网络的分析工具，以促进科学界对所提出方法的定量实施。
