计算机科学
空(SQL)
无效假设
工作流程
机器学习
置信区间
药物开发
统计假设检验
数据挖掘
人工智能
统计
药品
数学
医学
药理学
数据库
作者
Elli Makariadou,X WANG,Nicholas Hein,Negera Wakgari Deresa,Kathy Mutambanengwe,Bie Verbist,Olivier Thas
摘要
Combination treatments have been of increasing importance in drug development across therapeutic areas to improve treatment response, minimize the development of resistance, and/or minimize adverse events. Pre-clinical in-vitro combination experiments aim to explore the potential of such drug combinations during drug discovery by comparing the observed effect of the combination with the expected treatment effect under the assumption of no interaction (i.e., null model). This tutorial will address important design aspects of such experiments to allow proper statistical evaluation. Additionally, it will highlight the Biochemically Intuitive Generalized Loewe methodology (BIGL R package available on CRAN) to statistically detect deviations from the expectation under different null models. A clear advantage of the methodology is the quantification of the effect sizes, together with confidence interval while controlling the directional false coverage rate. Finally, a case study will showcase the workflow in analyzing combination experiments.
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