Evaluation of Bayesian adaptive designs for Phase 3 effectiveness trials

临床试验耗时、困难且昂贵，是改善患者治疗的重大障碍。科学家和资助者已经认识到需要提高试验的效率，然而绝大多数试验仍在使用传统方法。一种不同的试验方法，贝叶斯适应性试验方法，在过去的20年里已经发展起来，它有可能让试验更有效地回答他们的问题，这通常意味着可以用更少的病人和更短的时间来确定有效性。该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，并随后对试验进行修改。例如，可以改变分配到每种干预措施的参与者的比例，以确保参与者的分配最有效；如果已经清楚地表明随机分配到一个手臂是无效的，可以停止随机分配；或者如果达到了目的，可以提前终止整个试验。这些调整可能使试验更有效地进行，并允许用相同的资源评估更多的治疗方法。在这个项目中，我们的目标是评估贝叶斯自适应试验方法，以找出它们是否有可能在规模、持续时间和成本方面为试验者和资助者带来实际利益。如果是这样的话，将有一个强有力的案例来更广泛地使用这种方法，并且它可能在将来成为标准的方法。为了评估这些方法，我们将使用由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，然后进行广泛的模拟以了解其性能并微调其操作参数，如分析的数量和时间，以及决策的阈值。一组没有参与原始试验的临床医生和研究人员将选择一个单一的首选设计。然后将使用原始患者序列重新进行试验，并将重新进行的试验与原始总持续时间、患者数量、结果、接受更有利治疗的患者数量和试验成本进行比较。研究1将涉及四个案例研究，使用具有不同问题的试验：一个因干预出现有害而早期停止的试验，一个多臂试验，一个得出干预优势的试验，以及一个招募了很长时间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，但将纳入已完成招募但尚未报道的试验；因此，他们的结果还不得而知。研究3将涉及重新设计使用传统设计的试验，这些试验正在开始或处于招募的早期阶段。在实际试验进行的同时，随着试验的进行，我们将根据重新设计的时间表实时执行贝叶斯中期分析。这将显示贝叶斯自适应设计是否会导致与传统设计不同的行为、结果和结论。在审判结束之前，这些“影子”分析的结果不会公布。

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.