Institute of Psychiatry, King's College London, London, UK.
MRC Clinical Trials Unit at UCL, London, UK.
Stat Med. 2023 Apr 15;42(8):1156-1170. doi: 10.1002/sim.9663. Epub 2023 Feb 2.
In some clinical scenarios, for example, severe sepsis caused by extensively drug resistant bacteria, there is uncertainty between many common treatments, but a conventional multiarm randomized trial is not possible because individual participants may not be eligible to receive certain treatments. The Personalised Randomized Controlled Trial design allows each participant to be randomized between a "personalised randomization list" of treatments that are suitable for them. The primary aim is to produce treatment rankings that can guide choice of treatment, rather than focusing on the estimates of relative treatment effects. Here we use simulation to assess several novel analysis approaches for this innovative trial design. One of the approaches is like a network meta-analysis, where participants with the same personalised randomization list are like a trial, and both direct and indirect evidence are used. We evaluate this proposed analysis and compare it with analyses making less use of indirect evidence. We also propose new performance measures including the expected improvement in outcome if the trial's rankings are used to inform future treatment rather than random choice. We conclude that analysis of a personalized randomized controlled trial can be performed by pooling data from different types of participants and is robust to moderate subgroup-by-intervention interactions based on the parameters of our simulation. The proposed approach performs well with respect to estimation bias and coverage. It provides an overall treatment ranking list with reasonable precision, and is likely to improve outcome on average if used to determine intervention policies and guide individual clinical decisions.
在某些临床情况下，例如由广泛耐药菌引起的严重败血症，许多常见治疗方法之间存在不确定性，但由于个体参与者可能不符合接受某些治疗方法的条件，因此常规的多臂随机试验是不可能的。个性化随机对照试验设计允许每个参与者在适合他们的“个性化随机化列表”中的治疗方法之间进行随机化。主要目的是生成可以指导治疗选择的治疗排序，而不是关注相对治疗效果的估计。在这里，我们使用模拟来评估这种创新试验设计的几种新的分析方法。其中一种方法类似于网络荟萃分析，其中具有相同个性化随机化列表的参与者就像一个试验一样，同时使用直接和间接证据。我们评估了这种拟议的分析，并将其与较少使用间接证据的分析进行了比较。我们还提出了新的性能指标，包括如果使用试验的排序来告知未来的治疗而不是随机选择，预期改善结果的程度。我们得出结论，个性化随机对照试验的分析可以通过合并来自不同类型参与者的数据来完成，并且基于我们的模拟参数，对适度的亚组间干预相互作用具有稳健性。所提出的方法在估计偏差和覆盖范围方面表现良好。它提供了一个具有合理精度的总体治疗排序列表，如果用于确定干预策略并指导个体临床决策，平均而言可能会改善结果。
