Practical guidance on accessible statistical methods for different estimands in randomised trials

随机对照试验(RCT)包括将患者随机分配到不同的治疗方案。由于随机分配，平均而言，治疗组之间的患者因素(如年龄、性别和疾病阶段)是相同的，这意味着两组之间的结果(如死亡率)的任何差异很可能是由于治疗造成的。正因为如此，随机对照试验被认为是产生新疗法是否安全有效的高质量证据的黄金标准。然而，在大多数随机对照试验中，患者会经历计划外的事件，这意味着他们不得不提前停止治疗，从一个治疗切换到另一个治疗，或者在试验之外接受额外的治疗。这可能会使结果的解释复杂化，因为在计算治疗效果时，并不总是清楚如何处理这种计划外事件。例如，无论患者是继续接受治疗还是转向其他治疗，开始治疗的影响都可以计算出来。或者，也可以计算实际完成一个疗程的效果。大多数试验没有明确说明他们计算出的治疗效果，这可能会导致误解。药品监管机构和制药业最近制定了新的国际指南，以强调明确报告治疗效果解释的重要性。然而，指南没有提供计算这些治疗效果的统计方法。虽然已经提出了一些统计方法来计算不同的治疗效果，但这些方法往往以过于技术性的方式编写，发表在那些进行试验的人不熟悉的学术期刊上，并且没有提供实施这些方法所需的计算机代码。因此，许多试验使用了不适当的方法来计算治疗效果。因此，迫切需要指导适当的、可获得的统计方法来计算随机对照试验的治疗效果。

Randomised controlled trials (RCTs) involve assigning patients at random to different treatments. Because of the random assignment, patient factors (such as age, sex, and disease stage) will, on average, be the same between treatment groups, meaning that any differences in outcomes (such as mortality) between the groups will likely be due to the treatment. Because of this, RCTs are considered the gold standard for generating high-quality evidence on whether new treatments are safe and effective.However, during most RCTs patients will experience unplanned events which mean they have to stop treatment early, switch from one treatment to another, or receive additional treatments outside of the trial. This can complicate the interpretation of results, as it is not always clear how such unplanned events are handled in the calculation of the treatment effect. For instance, the effect of starting a patient on the treatment, regardless of whether they continue taking treatment or switch to something else, could be calculated. Alternatively, the effect of actually completing a course of treatment could also be calculated. Most trials do not explicitly state which treatment effect they have calculated, which can lead to misinterpretations.New international guidelines have recently been developed by drug regulators and the pharmaceutical industry to highlight the importance of clear reporting of the treatment effect's interpretation. However, the guidance does not provide the statistical methods for calculating these treatment effects. Whilst some statistical methods have been proposed for calculating different treatment effects, these are often written in an overly technical manner, are published in academic journals unfamiliar to those running trials, and do not provide the computer code required to implement such methods. Thus, many trials use inappropriate methods to calculate treatment effects. As such, there is urgent need for guidance on appropriate, accessible, statistical methods to calculate treatment effects in RCTs.