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

随机对照试验（RCTs）涉及随机分配患者接受不同的治疗。由于随机分配，患者因素（如年龄、性别和疾病阶段）在治疗组之间平均是相同的，这意味着组之间结果（如死亡率）的任何差异都可能是由于治疗。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金标准。然而，在大多数随机对照试验中，患者会遇到计划外的事件，这意味着他们必须提前停止治疗，从一种治疗转向另一种治疗，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如何处理这些计划外事件并不总是很清楚。例如，可以计算出开始治疗的患者的效果，无论他们是继续接受治疗还是转向其他治疗。或者，实际完成一个疗程的效果也可以计算出来。大多数试验没有明确说明他们计算了哪种治疗效果，这可能导致误解。药品监管机构和制药业最近制定了新的国际准则，以强调明确报告治疗效果解释的重要性。然而，该指南并未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，但这些方法通常是以过于技术性的方式编写的，发表在那些不熟悉试验的学术期刊上，并且没有提供实施这些方法所需的计算机代码。因此，许多试验使用不恰当的方法来计算治疗效果。因此，迫切需要关于计算随机对照试验治疗效果的适当、可获得的统计方法的指导。

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.