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Hard, harder, hardest: principal stratification, statistical identifiability, and the inherent difficulty of finding surrogate endpoints

机译：难，难，最难：主体分层，统计可识别性以及寻找替代终点的固有困难

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In many areas of clinical investigation there is great interest in identifying and validating surrogate endpoints, biomarkers that can be measured a relatively short time after a treatment has been administered and that can reliably predict the effect of treatment on the clinical outcome of interest. However, despite dramatic advances in the ability to measure biomarkers, the recent history of clinical research is littered with failed surrogates. In this paper, we present a statistical perspective on why identifying surrogate endpoints is so difficult. We view the problem from the framework of causal inference, with a particular focus on the technique of principal stratification (PS), an approach which is appealing because the resulting estimands are not biased by unmeasured confounding. In many settings, PS estimands are not statistically identifiable and their degree of non-identifiability can be thought of as representing the statistical difficulty of assessing the surrogate value of a biomarker. In this work, we examine the identifiability issue and present key simplifying assumptions and enhanced study designs that enable the partial or full identification of PS estimands. We also present example situations where these assumptions and designs may or may not be feasible, providing insight into the problem characteristics which make the statistical evaluation of surrogate endpoints so challenging.
机译：在临床研究的许多领域中，人们非常关注识别和验证替代终点，这些生物终点可以在治疗后的较短时间内进行测量，并且可以可靠地预测出治疗对目标临床结果的影响。然而，尽管在测量生物标志物的能力方面取得了巨大的进步，但最近的临床研究历史上充斥着失败的替代物。在本文中，我们提出了一个统计角度，说明为什么很难确定替代终点。我们从因果推理的框架中看待这个问题，特别关注主体分层技术（PS），该方法之所以具有吸引力，是因为由此产生的估计值不会因不可估量的混淆而产生偏差。在许多情况下，PS估计值在统计上是不可识别的，其不可识别程度可被认为代表了评估生物标志物替代价值的统计难度。在这项工作中，我们研究了可识别性问题，并提出了关键的简化假设和增强的研究设计，这些研究使得能够部分或完全识别PS估计值。我们还将介绍这些假设和设计可能可行或可能不可行的示例情况，以深入了解问题特征，这些问题特征使得代理端点的统计评估非常具有挑战性。

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