Biostatistics and Pharmacometrics, Novartis Pharma AG, Basel, Switzerland.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Stat Med. 2019 Oct 15;38(23):4761-4771. doi: 10.1002/sim.8333. Epub 2019 Aug 6.
The treatment effect in subgroups of patients is often of interest in randomized controlled clinical trials, as this may provide useful information on how to treat which patients best. When a specific subgroup is characterized by the absence of certain events that happen postrandomization, a naive analysis on the subset of patients without these events may be misleading. The principal stratification framework allows one to define an appropriate causal estimand in such settings. Statistical inference for the principal stratum estimand hinges on scientifically justified assumptions, which can be included with Bayesian methods through prior distributions. Our motivating example is a large randomized placebo-controlled trial of siponimod in patients with secondary progressive multiple sclerosis. The primary objective of this trial was to demonstrate the efficacy of siponimod relative to placebo in delaying disability progression for the whole study population. However, the treatment effect in the subgroup of patients who would not relapse during the trial is relevant from both a scientific and patient perspective. Assessing this subgroup treatment effect is challenging as there is strong evidence that siponimod reduces relapses. We describe in detail the scientific question of interest, the principal stratum estimand, the corresponding analysis method for binary endpoints, and sensitivity analyses. Although our work is motivated by a randomized clinical trial, the approach has broader appeal and could be adapted for observational studies.
在随机对照临床试验中，患者亚组的治疗效果通常是研究人员感兴趣的，因为这可能提供有关如何针对最佳患者进行治疗的有用信息。当特定亚组的特点是没有发生随机化后某些事件时，对没有这些事件的患者子集进行单纯分析可能会产生误导。主要分层框架允许在这种情况下定义适当的因果估计量。主要分层估计量的统计推断取决于有科学依据的假设，可以通过先验分布将这些假设纳入贝叶斯方法。我们的示例是一项大型随机安慰剂对照西尼莫德治疗继发进展型多发性硬化症患者的临床试验。该试验的主要目的是证明西尼莫德相对于安慰剂在延迟整个研究人群的残疾进展方面的疗效。然而，从科学和患者角度来看，在试验期间不会复发的患者亚组的治疗效果是相关的。评估该亚组的治疗效果具有挑战性，因为有强有力的证据表明西尼莫德可减少复发。我们详细描述了感兴趣的科学问题、主要分层估计量、针对二分类结局的相应分析方法以及敏感性分析。虽然我们的工作是由一项随机临床试验驱动的，但该方法具有更广泛的吸引力，并且可以适用于观察性研究。
