ANALYSIS OF LONGITUDINAL TRIALS WITH PROTOCOL DEVIATION: A FRAMEWORK FOR RELEVANT, ACCESSIBLE ASSUMPTIONS, AND INFERENCE VIA MULTIPLE IMPUTATION
ANALYSIS OF LONGITUDINAL TRIALS WITH PROTOCOL DEVIATION: A FRAMEWORK FOR RELEVANT, ACCESSIBLE ASSUMPTIONS, AND INFERENCE VIA MULTIPLE IMPUTATION
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DOI：
10.1080/10543406.2013.834911
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发表时间：
2013-11-02
影响因子：
1.1
通讯作者：
Kenward, Michael G.
中科院分区：
文献类型：
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作者：
Carpenter, James R.;Roger, James H.;Kenward, Michael G.
方案偏离，例如，由于提前退出和不依从，是不可避免的临床试验。这种偏差往往导致数据缺失。因此，分析需要额外的假设，而这些假设无法从现有数据中得到明确验证。因此，正如最近的监管指南和报告所承认的那样，明确这些假设及其影响对于主要分析和制定相关敏感性分析至关重要。这篇文章的重点是纵向定量结局数据的临床试验。对于目标人群，我们定义了两个被估量，法律上的被估量，在最好的情况下治疗的工作，和事实上的被估量，在实践中看到的效果。然后，我们仔细定义了与被估量相关的偏离方案的概念，或简称为偏离。然后，每个患者的随机化后数据可以分为偏差前数据和偏差后数据。我们为与事实和法律上的被估量相关的上下文适当的假设制定了一个可访问的框架，即关于与手头的临床问题相关的偏差前和偏差后数据的联合分布的假设。然后，我们将展示如何在这些假设下，多重插补提供了一个实用的方法来估计和推断。我们用慢性哮喘患者的纵向临床试验数据来说明。
Protocol deviations, for example, due to early withdrawal and noncompliance, are unavoidable in clinical trials. Such deviations often result in missing data. Additional assumptions are then needed for the analysis, and these cannot be definitively verified from the data at hand. Thus, as recognized by recent regulatory guidelines and reports, clarity about these assumptions and their implications is vital for both the primary analysis and framing relevant sensitivity analysis. This article focuses on clinical trials with longitudinal quantitative outcome data. For the target population, we define two estimands, the de jure estimand, does the treatment work under the best case scenario, and the de facto estimand, what would be the effect seen in practice. We then carefully define the concept of a deviation from the protocol relevant to the estimand, or for short a deviation. Each patient's postrandomization data can then be divided into predeviation data and postdeviation data. We set out an accessible framework for contextually appropriate assumptions relevant to de facto and de jure estimands, that is, assumptions about the joint distribution of pre- and postdeviation data relevant to the clinical question at hand. We then show how, under these assumptions, multiple imputation provides a practical approach to estimation and inference. We illustrate with data from a longitudinal clinical trial in patients with chronic asthma.