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道德的替代方案。在整个试验过程中，允许对设计进行计划调整，以解决伦理、可行性和科学问题。具体而言，在响应自适应设计中，中间结果用于调整臂分配比率或做出停止决策。贝叶斯方法已成为设计和分析反应适应性试验的默认方法，由于以下原因：在贝叶斯框架中便于顺序更新结果;贝叶斯显著性检验中自然处理了多个临时外观以及多组试验设置产生的多重性;很容易解决不确定性量化问题;先验知识可以通过贝叶斯模型很容易地合并。然而，目前，贝叶斯分析的适应性临床试验仅限于简单的模型与封闭形式的后验，和实用的贝叶斯推理的创新和有效的设计和分析的临床试验还有待探讨。 在贝叶斯适应性试验中，决策依赖于贝叶斯后验或预测概率。然而，主要的设计操作特性（DOC）仍然是频率主义的措施，即功率和I型错误率。因此，计划试验的关键步骤是指定符合DOC要求的停止/调整规则。由于贝叶斯检验统计量的抽样分布一般不为人所知，因此无法通过分析获得“最优”贝叶斯规则。目前，通过广泛的探索性模拟研究来指定决策标准。 建议的研究计划的重点是一个全面的决策理论方法贝叶斯自适应临床试验的优化设计。目标是开发严格的方法，计算技术和可访问的软件，可以使用，无论模型的复杂性。 更具体地说，我建议模拟DOC的高斯过程替代模型建立在模拟试验的少量决策标准。有前途的发展途径包括将已知的DOC行为的替代模型，通过顺序设计框架，多变量替代建模和不确定性量化的细化步骤的方法。此外，针对本问题的优化程序将被开发。该提案提出了一种贝叶斯顺序优化方法，其中利用有关DOC行为的先验信息来通知搜索，同时自适应地减轻不确定性。 拟议计划的完成将通过为研究者提供一套完整的方法和可访问的工具来进行适应性试验设计，从而消除设计高效和道德临床试验的主要障碍，并将使HQP具有异常多样化和专业化的技能。

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.