Modern Techniques in Design and Analysis of Bayesian Adaptive Clinical Trials

适应性设计被认为是传统随机临床试验的一种有效和合乎伦理的替代方案。在试验过程中，允许对设计进行有计划的调整，以解决伦理、可行性和科学问题。具体地说，作为响应，自适应设计的中间结果被用来调整手臂分配比例或做出停车决策。由于以下原因，贝叶斯方法已成为反应自适应试验设计和分析的默认方法：在贝叶斯框架中便于顺序更新结果；在贝叶斯显著性检验中自然地处理由多个临时观察以及多臂试验设置引起的多重性；易于解决不确定性量化问题；以及可通过贝叶斯模型容易地纳入先验知识。然而，目前对适应性临床试验的贝叶斯分析仅限于具有闭合后验的简单模型，而贝叶斯推断用于创新和有效地设计和分析临床试验还有待探索。 在贝叶斯适应性试验中，决策依赖于贝叶斯后验概率或预测概率。然而，主要的设计工作特性(DOC)仍然是频率测量，即功率和I类错误率。因此，规划试验的一个关键步骤是指定满足DOC要求的停止/适应规则。由于贝叶斯检验统计量的抽样分布一般不为人所知，因此不能通过解析获得“最优”的贝叶斯规则。目前，决策标准是通过广泛的探索性模拟研究来确定的。 建议的研究计划集中在贝叶斯适应性临床试验优化设计的综合决策理论方法上。目标是开发严格的方法、计算技术和可访问的软件，无论模型的复杂程度如何，都可以使用。 更具体地说，我建议通过高斯过程代理模型来模拟DOC，该模型建立在针对少量决策标准的试验模拟基础上。有前景的发展途径包括将已知的DOC行为纳入替代模型的方法、通过顺序设计框架进行改进的步骤、多变量替代建模和不确定性量化。此外，还将制定针对当前问题的优化程序。该提案提出了一种贝叶斯序贯优化方法，其中利用关于DOC行为的先验信息来为搜索提供信息，同时自适应地减轻不确定性。 拟议计划的完成将为研究人员提供一整套用于适应性试验设计的方法学和可获得的工具，从而消除设计高效和合乎道德的临床试验的主要障碍，并将导致HQP具有异常多样化和专业化的技能集。

Adaptive designs are considered an efficient and ethical alternative to conventional randomized clinical trials. Planned adjustments to the design are allowed through the course of the trial to address ethical, feasibility and scientific issues. Specifically, in response adaptive designs intermediate results are used to adapt arm allocation ratios or make stopping decisions. Bayesian methods have become the default approach for design and analysis of response adaptive trials due to the following reasons: sequentially updating the results is facilitated in the Bayesian framework; multiplicity arising from multiple interim looks as well as multi-arm trial settings is naturally dealt with in Bayesian significance tests; uncertainty quantification is easily addressed; and prior knowledge can be readily incorporated through Bayesian models. However, currently, Bayesian analyses for adaptive clinical trials are restricted to simple models with closed form posteriors, and utilities of Bayesian inference for innovative and efficient design and analysis of clinical trials are yet to be explored. In Bayesian adaptive trials, decision making relies on Bayesian posterior or predictive probabilities. However, the main design operating characteristics (DOC) remain frequentist measures, namely power and type I error rate. A crucial step in planning the trial is, therefore, specifying the stopping/adaptation rules that meet DOC requirements. Given that the sampling distributions of Bayesian test statistics are not generally known, the "optimal" Bayesian rules cannot be obtained analytically. Currently, decision criteria are specified by means of extensive, exploratory simulation studies. The proposed research program is focused on a comprehensive decision theoretic approach for optimal design of Bayesian adaptive clinical trials. The goal is to develop rigorous methodology, computational techniques and accessible software that can be used regardless of complexity of the model. More specifically, I propose emulating the DOC by Gaussian process surrogate models built upon simulations of the trial for a small number of decision criteria. Promising development avenues include methods for incorporating known DOC behaviour in the surrogate model, refinement steps through a sequential design framework, multivariate surrogate modelling and uncertainty quantification. Furthermore, optimization procedures tailored to the present problem will be developed. The proposal lays out a Bayesian sequential optimization approach in which prior information about the behaviour of DOC are leveraged to inform the search while mitigating uncertainty adaptively. The completion of the proposed program will remove major roadblocks in design of efficient and ethical clinical trials by providing investigators with a complete set of methodology and accessible tools for adaptive trial design and will lead to HQP with an exceptionally diverse and specialized skill set.