Evaluation of Bayesian adaptive designs for Phase 3 effectiveness trials

临床试验耗时、困难、昂贵，是改善患者治疗的重大障碍。研究人员和资助者已经认识到提高试验效率的必要性，但绝大多数试验仍在使用传统方法。另一种不同的试验方法，贝叶斯自适应试验方法，是在过去20年中开发的，有可能使试验更有效地回答他们的问题，通常意味着可以在更少的患者和更短的时间内确定有效性。这一方法使用贝叶斯统计方法，并包括频繁的中期分析，以便从迄今收集的试验数据中学习，并在随后对试验进行修改。例如，可以改变分配给每个干预措施的参与者的比例，以确保参与者得到最有效的分配，如果明显无效，可以停止随机分配到一组参与者，或者如果整个试验达到目的，可以提前终止整个试验。我们在这个项目中的目标是评估贝叶斯自适应试验方法，找出它们是否可能从规模、持续时间和成本方面为试验者和资助者带来实际好处。如果是这样的话，有很强的理由更广泛地使用这种方法，它可能成为未来的标准方法。为了评估这些方法，我们将使用华威临床试验单位进行的试验数据进行三项研究。在研究1中，每个试验都将使用贝叶斯自适应方法进行重新设计，这将涉及产生许多候选设计，然后执行广泛的模拟以了解其性能并微调其操作参数，如分析的次数和时间以及决策的阈值。一个单一的首选设计将由一组临床医生和研究人员选出，他们没有参与最初的试验。然后，我们将使用原始的患者序列重新运行试验，并将重新运行的试验与最初的总体持续时间、患者数量、结果、接受更有利治疗的患者数量以及试验的成本进行比较。研究1将涉及四个案例研究，使用的试验有不同的问题：一项因干预似乎有害而提前停止的试验，一项多武器试验，一项得出干预优势的试验，以及一项长期招募但没有显示差异的试验。研究2将使用与研究1相同的方法，但将涉及已完成招募但尚未报告的试验；因此其结果尚不清楚。研究3将涉及重新设计使用常规设计并正在开始招募或处于招募初期的试验。在进行实际试验的同时，随着试验的进行，我们将根据重新设计的时间表实时进行贝叶斯中期分析。这将表明贝叶斯自适应设计是否会导致不同于常规设计的行为、结果和结论。在试验结束之前，不会公布这些“影子”分析的结果。

Clinical trials are time consuming, difficult and expensive, and represent a substantial barrier to improving treatments for patients. Researchers and funders have recognised the need for trials to become more efficient, yet the overwhelming majority of trials continue to use traditional methods. A different approach to trials, Bayesian adaptive trial methodology, has been developed over the last 20 years, and has the potential to allow trials to answer their questions more efficiently, often meaning that effectiveness can be determined with fewer patients and in a shorter time. This approach uses Bayesian statistical methods, and includes frequent interim analyses, to allow learning from the trial data collected so far, and subsequent modification of the trial. For example, the proportion of participants allocated to each intervention could be changed to ensure that participants are allocated most efficiently, randomisation to one arm could be stopped, if it has become clear that it is not effective, or the whole trial could be terminated early if it has achieved its aim. These adaptations may enable trials to be run more efficiently, and allow evaluation of more treatments with the same resources.Our aim in this project is to evaluate Bayesian adaptive trial methods, to find out whether they are likely to lead to practical benefits for triallists and funders, in terms of their size, duration and cost. If so, there would be a strong case for using this methodology more widely, and it could potentially become the standard methodology in the future.To evaluate these methods we will perform three studies using data from trials run by the Warwick Clinical Trials Unit. In Study 1, each trial will be redesigned using Bayesian adaptive methods, which will involve producing a number of candidate designs, then performing extensive simulations to understand their performance and to fine-tune their operational parameters such as number and timing of analyses, and threshold values for decisions. A single preferred design will be selected by a group of clinicians and researchers who were not involved in the original trial. The trial will then be re-run, using the original sequence of patients, and we will compare the re-run trial with the original overall duration, number of patients, results, number of patients receiving the more favourable treatment, and cost of the trial. Study 1 will involve four case studies, using trials that had different issues: one that was stopped early because the intervention appeared harmful, one multi-armed trial, one that concluded superiority of its intervention, and one that recruited for a long time but showed no difference.Study 2 will use the same methods as Study 1, but will involve trials that have completed recruitment but have not yet been reported; their results will therefore not yet be known.Study 3 will involve re-designing trials that use a conventional design and are starting or in the early stages of recruitment. In parallel with the real-life trial conduct, we will performing Bayesian interim analyses according to the redesigned schedule as the trial proceeds, in real time. This will show whether the Bayesian adaptive design would lead to different conduct, results and conclusions from the conventional design. No results from these "shadow" analyses will be released until the trial has concluded.