Collaborative Research: Optimal Design of Experiments for Categorical Data

协作研究：分类数据实验的优化设计

• 批准号: 0707013
• 负责人: Min Yang
• 金额: $ 14.43万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0707013&HistoricalAwards=false
• 关键词: Collaborative; Research; Optimal; Design; Experiments

The investigators develop methods for identifying optimal and efficient designs for experiments with categorical data. The project consists of three main parts. (i) Identification of optimal designs for binary data under generalized linear regression models. This part includes consideration of models in which slope and intercept parameters can vary for different groups of subjects and models with a random subject effect. (ii) Identification of optimal allocations of treatments to blocks for comparative studies with binary data. A logistic model is a popular choice for such studies. (iii) Identification of optimal designs for count data under loglinear regression models. In this setting, the investigators focus also on optimal designs for models that can account for subject heterogeneity. This project is innovative in that it uses a new technique that has vast advantages over the commonly used geometric approach. Categorical responses are very common in designed experiments in many scientific studies, such as drug discovery, clinical trials, social sciences, marketing, etc. Generalized Linear Models (GLMs) are widely used for modeling such data. Using efficient designs for collecting data in such experiments is critically important. It can reduce the sample size needed for achieving a specified precision, thereby reducing the cost, or improve the precision of estimates for a specified sample size. While research on optimal designs for linear models has been systematically developed over more than 30 years, there are very few research publications on optimal designs for GLMs. This project is important both for the introduction of novel theoretical tools and for its impact on applications. For example, the results of the project significantly reduce the time, money, and the number of patients needed in clinical trials, as well as other scientific studies. The results can help the U.S. Food and Drug Administration to improve its guidelines for clinical trials.

研究人员开发了识别具有分类数据实验的最佳和高效设计的方法。该项目由三个主要部分组成。 （i）在广义线性回归模型下识别二进制数据的最佳设计。该部分包括考虑模型的考虑，其中斜率和截距参数可能会因随机受试者效应而不同。 （ii）用二进制数据鉴定对比较研究的最佳治疗分配。逻辑模型是此类研究的流行选择。 （iii）在loglinear回归模型下识别计数数据的最佳设计。在这种情况下，调查人员还专注于可以解释主题异质性的模型的最佳设计。该项目具有创新性，因为它使用了一种与常用几何方法相比具有巨大优势的新技术。在许多科学研究的设计实验中，分类反应非常普遍，例如药物发现，临床试验，社会科学，市场营销等。广义线性模型（GLM）广泛用于建模此类数据。在此类实验中使用有效的设计收集数据至关重要。它可以减少实现指定精度所需的样本量，从而降低成本，或提高指定样本量的估计精度。虽然在30多年来系统地开发了对线性模型最佳设计的研究，但关于GLM的最佳设计的研究出版物很少。该项目对于引入新型理论工具及其对应用的影响都很重要。例如，该项目的结果大大减少了临床试验中的时间，金钱以及所需的患者数量以及其他科学研究。结果可以帮助美国食品药品监督管理局改善其临床试验准则。