Abstract

After completing the experimental runs of a screening design, the responses under study are analyzed by statistical methods to detect the active effects. To increase the chances of correctly identifying these effects, a good analysis method should provide alternative interpretations of the data, reveal the aliasing present in the design, and search only meaningful sets of effects as defined by user-specified restrictions such as effect heredity. This article presents a mixed integer optimization strategy to analyze data from screening designs that possesses all these properties. We illustrate our method by analyzing data from real and synthetic experiments, and using simulations.

摘要

在完成筛选设计的实验运行后，通过统计方法分析所研究的响应以检测活性效应。为了增加正确识别这些效应的机会，一个好的分析方法应该提供数据的替代解释，揭示设计中存在的混叠，并仅搜索由用户指定的限制（如效应遗传）定义的有意义的效应集。本文介绍了一种混合整数优化策略，用于分析来自具有所有这些属性的筛选设计的数据。我们通过分析来自真实和合成实验的数据并使用模拟来说明我们的方法。