Statistical Modelling for Adaptive Design of Clinical Trials: Optimality and Efficiency

与传统的临床试验方法相比，响应自适应设计(RAD)具有伦理优势，但它也将相关性引入到数据中。大多数关于RAD的统计文献都集中在均值反应模型的统计方法上，而没有考虑群体的异质性。忽视这种异质性可能会在结论中引入偏见。此外，暴露在不同环境因素下的患者对治疗的反应可能会非常不同。所有这些都是随机临床试验推荐的有效治疗方法在日常保健中并不总是奏效的主要原因。 这项拟议的研究将开发新的RAD来宣传RAD的伦理优势，提出统计方法来解释RAD对各种种群异质性的依赖，并为所提议的设计建立有效的推理方法。本研究将探讨自适应随机化对统计估计的影响，并利用这种影响来提高学习效率对治疗效果的影响。这项研究考虑的群体异质性包括患者和机构的特征及其与治疗的互动，以及患者对一系列治疗的反应。 这项研究将建立具有协变量和交互作用的RAD的统计方法，特别是定性交互作用，以及动态治疗区域临床试验的RAD。这一结果将增加关于自适应随机化下的相依数据估计以及针对不同人群的临床试验的最佳设计的统计文献。使用RAD自适应地学习定性交互作用可能会提高检测子种群的效率。为分组反应开发的统计方法将为多中心试验增加有效的伦理设计。将自适应随机化方法整合到动态治疗区域临床试验中，可以在最优动态治疗区域上进行自适应学习，从而提高伦理效益和统计效率。 这项拟议的研究解决了面对群体异质性时临床试验设计和分析中的方法学挑战。所开发的方法将人口异质性纳入RAD学习，以倡导伦理利益并提高统计能力。这项研究将为统计文献增加临床试验的创新设计和有效的统计方法。所得到的工具箱可以推广到其他临床环境，在这些环境中，从业者希望有效地确定针对亚群的最佳治疗方法。

Response adaptive design (RAD) has ethical advantages over traditional methods for clinical trials, but it also introduces dependency into data. Most statistical literature on RAD focused on statistical methodologies for mean response models without considering population heterogeneity. Ignoring such heterogeneity could introduce bias into conclusions. In addition, patients exposed to different environmental factors may respond to treatments very differently. All these are the main reasons why the efficacious treatments recommended by randomized clinical trials do not always work in day-to-day health care. This proposed research will develop new RAD to advocate the ethical advantages of RAD, propose statistical methods to account for the dependency introduced by RAD for various population heterogeneities, and establish efficient inferential methods for the proposed designs. This research will investigate the influence of adaptive randomization on statistical estimation, and employ that influence to improve the efficiency in learning on treatment effect. The population heterogeneities considered in this research include patients’ and institutional characteristics and their interactions with treatments as well as patients' responses to a series of treatments. This research will establish statistical methods for RAD with covariates and interaction, especially qualitative interaction, as well as for RAD for dynamic treatment region clinical trials. The results will add to the statistical literature on estimation for dependent data under adaptive randomization as well as optimal designs of clinical trials for heterogeneous populations. The use of RAD to adaptively learn on qualitative interaction may improve the efficiency of detecting subpopulations. Statistical methods developed for clustered responses will add efficient ethical designs for multi-center trials. The integration of adaptive randomization into clinical trials of dynamic treatment region (DTR) will enable adaptive learning on the optimal DTR thus improving the ethical benefit and statistical efficiency. This proposed research addresses the methodological challenges in design and analysis of clinical trials when facing population heterogeneities. The developed methodologies incorporate population heterogeneities in RAD learning to advocate the ethical benefits and to improve statistical power. This research will add innovative designs of clinical trials and efficient statistical methods to statistical literature. The resulting toolbox can be generalized to other clinical settings where practitioners hope to efficiently identify optimal treatments for subpopulations.