Walter Stephen D, Turner Robin M, Macaskill Petra
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Biostatistics Unit, Division of Health Sciences, University of Otago, Dunedin, New Zealand.
Stat Med. 2019 Jun 15;38(13):2317-2331. doi: 10.1002/sim.8119. Epub 2019 Feb 22.
Outcomes in a clinical trial can be affected by any underlying preferences that its participants have for the treatments under comparison and by whether they actually receive their preferred treatment. These effects cannot be evaluated in standard trial designs but are estimable in the alternative two-stage randomised trial design, in which some patients can choose their treatment, while the rest are randomly assigned. We have previously shown that, when all two-stage trial participants have a preferred treatment, the preference effects can be evaluated, in addition to the usual direct effect of treatment. We also determined criteria by which to optimise how many participants should be given a choice of treatment vs being randomised. More recently, we extended our methodology to allow for participants who are unable or unwilling to express a treatment preference if they are assigned to the choice group. In this paper, we show how to optimise the two-stage design when some participants are undecided about their treatment. We demonstrate that the undecided group should be regarded as distinct in the analysis, to obtain valid estimates of the preference effects. We derive the optimal proportion of participants who should be offered a choice of treatment, which in many cases will be close to 50%. More generally, the optima depend on the preference rates for treatments and the proportion of undecided participants, and the parameters of primary interest. We discuss some advantages and disadvantages of the two-stage trial design in this situation and describe a practical example.
临床试验的结果可能会受到参与者对所比较治疗的任何潜在偏好的影响,以及他们是否实际接受了自己偏好的治疗。这些影响在标准试验设计中无法评估,但在另一种两阶段随机试验设计中是可估计的,在这种设计中,一些患者可以选择他们的治疗方法,而其余患者则被随机分配。我们之前已经表明,当所有两阶段试验参与者都有偏好的治疗方法时,除了治疗的通常直接效果外,还可以评估偏好效应。我们还确定了优化有多少参与者应被给予选择治疗方法与被随机分配的标准。最近,我们扩展了我们的方法,以允许如果被分配到选择组但无法或不愿意表达治疗偏好的参与者。在本文中,我们展示了在一些参与者对他们的治疗方法不确定时如何优化两阶段设计。我们证明在分析中应将不确定组视为不同的组,以获得偏好效应的有效估计。我们推导了应被给予治疗选择的参与者的最佳比例,在许多情况下该比例将接近50%。更一般地说,最佳比例取决于治疗的偏好率、不确定参与者的比例以及主要关注的参数。我们讨论了在这种情况下两阶段试验设计的一些优缺点,并描述了一个实际例子。