Robust and Efficient Assessment of Potency (REAP) as a quantitative tool for dose-response curve estimation

Abstract

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.

A comparative study of in vitro dose–response estimation under extreme observations

“…Apart from dose–response R packages, we are also interested in methods based on the beta regression, which was reported to provide robustness and efficiency against asymmetry, heteroscedasticity, and nonnormality in dose–response data (Zhou et al., 2022). However, given the recent rapid development of robust estimation approaches in the beta regression framework, most simulation studies were conducted only in a moderate sample (Ghosh, 2019; Niekerk et al., 2019; Ribeiro & Ferrari, 2020).…”

“…However, the development of the least squares method does not pay special attention to extreme cases, especially when the sample sizes are small. Recently, the beta regression model was introduced and incorporated into different statistical methods to yield robust estimation (Ghosh, 2019; Mayr et al., 2018; Niekerk et al., 2019; Ribeiro & Ferrari, 2020; Rigby & Stasinopoulos, 2005; Wood, 2017; Zhou et al., 2022). The construction of the beta regression itself is flexible enough to take on various shapes to account for nonnormality, asymmetry, and heteroskedasticity.…”

REAP-2: An interactive quantitative tool for robust and efficient dose-response curve estimation

“…We redesigned the user interface of REAP [3] via Cascading Style Sheets and updated the estimation algorithm based on the comparative study results by implementing the penalized beta regression with the mgcv package [7] to estimate the doseresponse. REAP-2 Shiny app is freely available and accessible at https://xi nying-fang.shinyapps.io/REAP/.…”

“…REAP takes on the beta law to account for non-normality and heteroscedasticity [4] and minimizes the average density power divergence with a tuning parameter [5]. Compared to the standard regression-based assessment, REAP proves to be more robust and powerful to extreme observations when estimating the median-effect equation [3].…”

“…To overcome the analytical challenge, we developed the Robust and Efficient Assessment of drug Potency (REAP) to estimate the median-effect equation based on the beta regression framework, along with a freely accessible, web-based R Shiny app [3]. REAP takes on the beta law to account for non-normality and heteroscedasticity [4] and minimizes the average density power divergence with a tuning parameter [5].…”