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有效和合乎道德的替代方案。在整个试验过程中，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。具体来说，在响应自适应设计中，中间结果用于调整武器分配比例或做出停止决策。由于以下原因，贝叶斯方法已经成为设计和分析响应适应性试验的默认方法：在贝叶斯框架中，顺序更新结果是方便的；在贝叶斯显著性检验中，由多个中期观察和多组试验设置引起的多重性自然得到处理；不确定度量化很容易解决；和先验知识可以很容易地通过贝叶斯模型纳入。然而，目前适应性临床试验的贝叶斯分析还局限于具有封闭后验的简单模型，贝叶斯推理在创新、高效的临床试验设计和分析中的应用还有待探索。在贝叶斯自适应试验中，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型错误率。因此，规划试验的关键步骤是指定符合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不为人所知，“最优”贝叶斯规则无法解析得到。目前，决策准则是通过广泛的探索性模拟研究来确定的。提出的研究计划侧重于贝叶斯适应性临床试验优化设计的综合决策理论方法。目标是开发严格的方法、计算技术和可访问的软件，无论模型的复杂性如何，都可以使用它们。更具体地说，我建议通过基于少量决策标准的试验模拟建立的高斯过程代理模型来模拟DOC。有希望的发展途径包括将已知的DOC行为纳入代理模型的方法，通过顺序设计框架的改进步骤，多变量代理建模和不确定性量化。此外，还将开发针对当前问题的优化程序。该建议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。该计划的完成将为研究人员提供一套完整的方法和可获得的适应性试验设计工具，从而消除设计高效和合乎伦理的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.