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MICA: Clinical trial estimands: from definition to estimation

MICA：临床试验估计值：从定义到估计

基本信息

批准号：
MR/T023953/1
负责人：
Jonathan Bartlett
金额：
$ 53.61万
依托单位：
University of Bath
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT023953%2F1
关键词：
MICA Clinical trial estimands definition

项目摘要

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.

随机临床试验代表了测试新的疾病治疗方法是否比现有治疗方法效果更好并量化获益程度的金标准方法。原则上，此类试验的分析很简单——将一组患者的所选结果指标与另一组患者的结果指标进行比较。在实践中，可能会出现许多复杂情况，使得这种比较难以解释，甚至无法计算。一个例子是，在试验中，患者可能会改变在随访期间随机分配接受的治疗，要么改为替代治疗，根本不接受治疗，要么开始接受额外的治疗。第二个例子是旨在比较（例如）胆固醇治疗对心血管疾病死亡影响的试验。由于一些患者可能死于其他原因，例如癌症，这种比较变得复杂。在一项简单的分析中，比较了两组之间因心血管疾病死亡的患者人数，例如，一种新的治疗方法可以减少因心血管疾病死亡的机会，但前提是它会增加因癌症引起的死亡。第三个例子是癌症试验，其兴趣在于比较治疗方法预防癌症复发的能力以及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测量的影响的比较都会变得复杂，因为每个治疗组中的一些患者将不可避免地无法获得这些测量，因为他们已经死亡。在这些问题的背景下，近年来，药物监管机构越来越严格地审查临床试验如何在设计和统计分析中指定他们将如何处理此类并发症。具体来说，越来越需要试验明确说明他们寻求量化的治疗效果（所谓的估计值），并选择一种以合理且合理的方式处理这些问题的统计分析方法。本研究的目的是调查如何使用所谓“因果推理理论”领域开发的概念和方法最好地处理此类并发症。该理论提供了一种数学语言，可以精确地描述在存在复杂因素（例如前面描述的因素）的情况下治疗效果的含义。此外，已经开发了多种统计方法来估计在不同假设下使用这些概念定义的治疗效果。本研究将使用因果推理理论在存在前面描述的各种问题的情况下精确定义治疗效果（估计值）。然后，它将调查因果推理理论中开发的哪些统计方法最适合应用于临床试验数据的分析。这项研究的成果将帮助参与临床试验的统计学家使用因果推理概念和语言来清楚地指定他们的试验打算估计的治疗效果。它将为他们提供指导和建议，告诉他们可以使用哪些统计方法来估计此类影响。该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试验中使用这些方法。这些成果共同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测量，并且临床医生可以就患者护理做出更明智的决定。该研究将使药品监管机构和付款机构能够在疗效、安全性和成本效益方面对治疗方法进行更公平的比较，从而改进关于向患者授予许可和提供哪些治疗方法的决策。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Standard and reference-based conditional mean imputation.

DOI：
10.1002/pst.2234
发表时间：
2022-11
期刊：
PHARMACEUTICAL STATISTICS
影响因子：
1.5
作者：
Wolbers, Marcel;Noci, Alessandro;Delmar, Paul;Gower-Page, Craig;Yiu, Sean;Bartlett, Jonathan W.
通讯作者：
Bartlett, Jonathan W.

Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods.