Design and analysis of repeated measures data

我的研究项目一直在重复测量数据的设计和分析的一般领域，并考虑了缺失数据分析，固定最优设计结构和响应自适应设计等问题。为了扩大范围，我最近考虑了一个更一般的模型来构造最优设计，因为最优设计是强烈依赖于模型的。我也考虑了适应数据中固有的异质性。在本提案中，我的研究计划的主要目标之一是将单目标设计扩展到多目标设计，以构建响应自适应设计。我计划开发一种新的自适应分配规则，可以提供准确和最佳的治疗效果对比的估计。这项工作的应用被发现在许多领域，如工程，农业，生物科学，以及生物医学science.Specifically，我将构造重复测量设计下的一般模型和文件的传统的最优设计的影响。一般模型将残留效应分为两种不同类型，一种是由上一阶段相同治疗给药引起的，另一种是由上一阶段其他竞争治疗给药引起的。迄今为止构造的大多数最优设计都假设这两种类型相等。此外，它们基于固定的样本量。我将应用拉格朗日乘子法并给出构建两个治疗p期重复测量设计的建议。将总结具有较少重复周期的实用设计。

My research program has been in the general area of design and analysis of repeated measures data, and has considered such issues as missing data analysis, fixed optimal design construction, and response adaptive designs. To broaden the scope, I have recently considered a more general model to construct optimal designs, as optimal designs are strongly model dependent. I also considered accommodating the heterogeneity inherent in the data.In this proposal, one of the main goals of my research program is to extend the single-objective designs to multiple-objective designs to build response-adaptive designs. I plan to develop a new adaptive allocation rule that can provide estimates of the treatment effect contrasts accurately and optimally. Application of this work is found in a number of fields such as engineering, agriculture, biosciences, as well as biomedical sciences.Specifically, I will construct repeated measurement designs under general models and document their impact on the traditional optimal designs. The general model will separate the carryover effects into two different types, one that results from the administration of the same treatment in the previous period and the other that results from the administration of the other competing treatment in the previous period. Most optimal designs constructed to date assume that these two types are equal. Further, they are based on fixed sample sizes. I will apply the Lagrange multiplier method and give recommendations on constructing two treatment p-period repeated measurement designs. Practically useful designs with fewer repeated periods will be summarized.