MICA: Clinical trial estimands: from definition to estimation

随机临床试验是测试新的疾病治疗方法是否比现有治疗方法更有效并量化益处大小的黄金标准方法。原则上，这类试验的分析很简单--比较一组患者与另一组患者所选择的结果指标。在实践中，可能会出现一些复杂的情况，使得这种比较难以解释，甚至不可能计算。一个例子是，在试验中，患者可能会从他们在随访期内被随机分配接受的治疗改为替代治疗，根本不治疗，或者他们可能开始接受额外的治疗(S)。第二个例子是旨在比较(例如)胆固醇治疗对因心血管疾病死亡的影响的试验。一些患者可能死于其他原因，如癌症，这一事实使这种比较变得复杂。在一项简单的分析中，比较了两组死于心血管疾病的患者数量，例如，一种新的治疗方法可以降低死于心血管疾病的机会，但前提是它增加了癌症死亡的事实。第三个例子是癌症试验，人们感兴趣的是比较治疗方法，包括它们预防癌症复发的能力和可能影响患者生活质量的不良副作用。任何治疗对患者生活质量指标影响的比较都会变得复杂，因为每个治疗组中的一些患者不可避免地无法使用这些措施，因为他们已经死亡。在这些问题的背景下，近年来，药品监管机构对临床试验如何在设计和统计分析中指定如何处理此类并发症进行了更严格的审查。具体地说，越来越多的人要求试验明确说明他们试图量化的治疗效果的种类(所谓的估计)，并选择一种以合理和可信的方式处理这些问题的统计分析方法。这项研究的目的是调查如何使用所谓的因果推理理论领域中发展的概念和方法来最好地处理这种并发症。这一理论提供了一种数学语言来准确描述我们所说的在存在复杂因素的情况下治疗的效果，例如前面描述的那些因素。此外，在不同的假设下，已经开发了大量的统计方法来估计使用这些概念定义的治疗效果。这项研究将使用因果推理理论来精确定义在存在前面描述的各种问题的情况下的治疗效果。这项研究的结果将帮助参与临床试验的统计学家使用因果推理的概念和语言来清楚地说明他们的试验打算估计的治疗效果。它将向他们提供指导和建议，说明他们可以使用哪些统计方法来估计这种影响。这项研究还将开发软件来实施新的统计方法，使试验统计学家能够在他们的试验中使用这些方法。总而言之，这些成果将意味着可以为患者提供更有意义和更准确的预期治疗效果衡量标准，临床医生可以对患者护理做出更明智的决定。这项研究将使药品监管机构和支付当局能够在有效性、安全性和成本效益方面对治疗进行更公平的比较，从而改进对哪些治疗发放许可证并提供给患者的决策。

Randomised clinical trials represent the gold standard approach for testing whether new treatments for diseases work better than existing treatments and quantifying the magnitude of the benefit. In principle the analysis of such trials is simple - one compares the chosen outcome measure of patients in one group with the patients in the other group. In practice a number of complications may arise which make this comparison difficult to interpret or impossible even to calculate. One example is trials in which patients may change from the treatment that they were randomly assigned to receive during the follow-up period, either to the alternative treatment, no treatment at all, or they may start taking additional treatment(s). A second example is in trials which aim to compare (for example) cholesterol treatments in terms of their effects on death due to cardiovascular disease. This comparison is complicated by the fact that some patients may die of other causes, such as cancer. In a simple analysis comparing the number of patients who died due to cardiovascular disease between the two groups, a new treatment could for example reduce the chances of death due to cardiovascular disease, but only by virtue of the fact it increases death due to cancer. A third example is trials in cancer where interest lies in comparing treatments both in terms of their ability to prevent cancer recurrence and in terms of their adverse side effects, which may impact on the patient's quality of life. Any comparison of the treatments' effects on patient quality of life measures is complicated by the fact that inevitably such measures will be unavailable for some patients in each treatment group because they have died.In the context of such issues, in recent years there has been increased scrutiny from drug regulatory agencies regarding how clinical trials specify how they will handle such complications in their design and statistical analysis. Specifically, there is an increased demand for trials to clearly specify exactly what kind of effect of treatment they seek to quantify (the so called estimand) and to choose a method of statistical analysis that handles these issues in a sensible and plausible manner.The aim of this research is to investigate how such complications can best be handled using concepts and methods developed in the field of so called 'causal inference theory'. This theory offers a mathematical language to precisely describe what we mean by the effect of treatment in the presence of complicating factors such as the ones described earlier. Moreover, a large range of statistical methods have been developed for estimating treatment effects defined using these concepts, under different assumptions. This research will use causal inference theory to precisely define treatment effects (estimands) in the presence of the various issues described earlier. It will then investigate which statistical methods developed in causal inference theory are best suited for application to the analysis of clinical trial data.The outputs of this research will help statisticians involved in clinical trials to use causal inference concepts and language to clearly specify the treatment effect which their trial intends to estimate. It will give them guidance and recommendations as to which statistical methods they can use to estimate such effects. The research will also produce software to implement the new statistical methods to enable trial statisticians to use the methods in their trials. Together these outputs will mean that patients can be offered more meaningful and accurate measures of expected treatment effects and that clinicians can make more informed decisions about patient care. The research will enable drug regulators and payer authorities to make fairer comparisons between treatments in regards their efficacy, safety, and cost-effectiveness, leading to improved decisions about which treatments to license and make available to patients.