Response adaptive designs with multiple objectives

在我的研究生涯中，我花了很多时间来研究在现实世界中实际适用的最佳设计。我的研究兴趣仍然是构建最优设计，同时处理各种实际挑战。在我之前工作的基础上，我计划进一步为二元响应构建响应自适应设计。该项目关注统计设计、建模和分析在重复测量数据的质量实验中的作用。该项目的主要目的是继续发展新的统计工具和方法，以解决实证科学研究中关注的实际问题。******传统上，优化设计文献集中于实现单一目标，即最小化估计效果的均方误差。然而，考虑到通过满足多个目标而获得的显著好处，我发现真的没有回到传统。需要更多的研究来为小样本自适应地建立最优设计，努力重新评估最优性，并分配更多的患者接受有益的治疗。此外，具有连续响应的响应自适应设计可以通过适应诸如测量误差和缺失数据方法等问题的可能解决方案而受益。******在我们最近的工作“二元反应临床试验中的多目标反应自适应重复测量设计”中，我们讨论了二元设计中由于误差矩阵依赖于参数而产生的独特特性。毫无疑问，这种依赖关系可以在最终的优化设计中发挥重要作用。协方差矩阵的不规范可能会影响设计效率，这将是我这部分研究的重点。因为这些实验通常是昂贵的，所以重点放在尽量减少实验努力和从结果分析中获得最大信息的技术和策略上。本研究将构建对各种实验条件具有鲁棒性的设计，并更好地理解统计最优性与实际最优性之间可能存在的冲突。实际优化设计的基础将进一步发展。将探索和发展各种其他统计技术，以处理具有重复测量数据的实际问题

I have spent much of my research career investigating optimal designs that are practically applicable in the real world. My research interests continue to be in constructing optimal designs, while dealing with various practical challenges. Building on my prior work, I plan to further work on building response adaptive designs for binary responses. The project concerns the role of statistical design, modelling, and analysis in quality experiments with repeated measures data. The primary purpose of this project is to continue the development of new statistical tools and methods that address real issues of concern in empirical scientific research. ******Traditionally, optimal design literature has focused on achieving a single objective, namely minimizing the mean square error of the estimated effects. However, considering significant benefits attainable by having multiple objectives satisfied, I find that there is really no going back to the tradition. More research is needed to build optimal designs for small samples adaptively on responses, placing efforts to reassess the optimality and allocating more patients to beneficial treatment(s). As well, response adaptive designs with continuous responses can be benefited by accommodating possible solutions on issues such as measurement errors and missing data methods. ******In our recent work “Multiple-objective response adaptive repeated measure designs in clinical trials for binary responses”, we discussed unique properties in binary designs that arise due to the error matrix being dependent on the parameters. No doubt, this dependency can play a significant role in the resulting optimal design. Misspecification of a covariance matrix may impact the design efficiency, and it will be a focus in this part of my research. Because these experimentations are usually expensive, emphasis is given to techniques and strategies which minimize experimental effort, and maximize information gained from analysis of the results. This research will construct designs that are robust to various experimental conditions with better understanding of possible conflicts between statistical and practical optimality. The foundations of practical optimal designs will be further developed. A variety of other statistical techniques to deal with real issues with repeated measures data will be explored and developed.**