Information-Rich and Cost-Effective Designs for Scientific Experiments

本项目的科学目标是提供新的统计理论和相关方法，（i）帮助药物化学家寻求更好的药代动力学和药效学（PK/PD）实验结果模型;（ii）减少生物制药（BE）和药物滥用责任试验中的实验变异性和成本;（iii）药物稳定性研究，包括药物的保质期估计。在此过程中，交叉设计和权衡理论的潜在应用将在广泛的制药，生物技术，农业和工程研究中得到加强。 研究活动综合运用了地理学、统计学和计算工具（部分已有，部分尚待开发），目的是指导实验人员（在制药、生物技术、农业和工程调查等领域）在进行计划的实验时收集信息丰富和成本效益高的数据。在实验设计和药物研究的广泛领域中提出的问题围绕着一些关键问题，这些问题在已发表的文献中几乎没有理论贡献。本文提出的每一个研究问题的特点是其独特性方面的创新工具，以开发和分析的论点，以通过。 这一提议的预期成果将有大量有用的应用，并将被科学家和工程师用于广泛的科学调查。

ABSTRACTThe scientific goals of the project are to provide new statistical theory and related methodology (i) for aiding medicinal chemists in seeking better models for pharmacokinetic and pharmacodynamic (PK/PD)experimental outcomes; (ii) for reducing the variability and cost of experiments in biopharmaceutical (BE) and drug abuse liability trials and (iii) in drug stability studies, including shelf life estimation of drugs. In the process, potential applications of cross-over designs and trade-off theory will be strengthened in a wide array of pharmaceutical, biotechnological, agricultural and engineering investigations. The research activities employ a combination of mathematica, statistical and computational tools (partly available and partly to be developed).The aim of this project is to guide experimenters (in areas such as pharmaceutical, biotechnological, agricultural and engineering investigations) towards collection of information-rich and cost-effectivedata while running planned experiments. The problems proposed in the broad area of design of experiments and pharmaceutical investigations center around some key issues for which very little theoreticalcontributions are available in the published literature. Every research problem proposed herein is characterized by its distinctiveness with respect to the innovative tools to be developed and analytical arguments to be adopted. The anticipated outcomes of this proposal will have substantial useful applications and will be utilized by scientists and engineers in a wide array of scientific investigations.