Department of Health Research Methodology, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.
Department of Mathematics & Statistics, McMaster University, Hamilton, ON, Canada.
Stat Methods Med Res. 2020 Dec;29(12):3783-3803. doi: 10.1177/0962280220941874. Epub 2020 Jul 23.
Recent work has shown that outcomes in clinical trials can be affected by which treatment the trial participants would select if they were allowed to do so, and if they do or do not actually receive that treatment. These influences are known as selection and preference effects, respectively. Unfortunately, they cannot be evaluated in conventional, parallel group trials because patient preferences remain unknown. However, several alternative designs have been proposed, to measure and take account of patient preferences. In this paper, we discuss three preference-based designs (the two-stage, fully randomised, and partially randomised designs). In conventional trials, only the treatment effect is estimable, while the preference-based designs have the potential to estimate some or all of the selection and preference effects. The relative efficiency of these designs is affected by several factors, including the proportion of participants who are undecided about treatments, or who are unable or unwilling to state a preference; the relative preference rate between the treatments being compared, among patients who do have a preference; and the ratio of patients randomised to each treatment. We also discuss the advantages and disadvantages of these designs under different scenarios.
最近的研究表明,临床试验的结果可能会受到以下因素的影响:如果试验参与者被允许这样做,他们会选择哪种治疗方法,以及他们实际上是否接受了这种治疗。这些影响分别被称为选择效应和偏好效应。不幸的是,由于患者的偏好仍然未知,这些影响在传统的平行组试验中无法评估。然而,已经提出了几种替代设计来测量和考虑患者的偏好。在本文中,我们讨论了三种基于偏好的设计(两阶段、完全随机和部分随机设计)。在传统试验中,只能估计治疗效果,而基于偏好的设计有可能估计部分或全部选择和偏好效应。这些设计的相对效率受到多个因素的影响,包括对治疗方法犹豫不决或无法或不愿表明偏好的参与者比例;在有偏好的患者中,比较的治疗方法之间的相对偏好率;以及随机分配到每种治疗方法的患者比例。我们还讨论了在不同情况下这些设计的优缺点。