Principal stratum strategy: Potential role in drug development
Principal stratum strategy: Potential role in drug development
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DOI：
10.1002/pst.2104
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发表时间：
2021-02-23
影响因子：
1.5
通讯作者：
Wolbers, Marcel
中科院分区：
文献类型：
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作者：
Bornkamp, Bjorn;Rufibach, Kaspar;Wolbers, Marcel
随机试验允许在总体人群和由基线特征定义的亚群中估计干预与对照的因果效应。然而，在随机化后可能发生临床或疾病相关事件的患者亚群中，通常也会出现关于治疗效果的临床问题。在ICH E9（R1）指导原则中，治疗开始后发生并可能影响解读或测量结果存在的事件称为并发事件。如果并发事件是治疗的结果，则单独随机化不再足以有意义地估计治疗效果。比较没有并发事件的患者亚组进行干预和对照的分析将不会估计因果效应。这是众所周知的，但这种事后分析通常在药物开发中进行。另一种方法是主要分层策略，根据两个研究组中并发事件的潜在发生率对受试者进行分类。我们举例说明，通过主要分层制定的问题自然发生在药物开发中，并认为，接近这些问题与ICH E9（R1）估计框架有可能导致更透明的假设，以及更充分的分析和结论。此外，我们概述估计主要阶层影响所需的假设。这些假设大多数是无法证实的，因此应该基于坚实的科学理解。需要进行敏感性分析，以评估结论的稳健性。
A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called intercurrent events in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions.