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首页> 外文期刊>Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology >Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials.
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Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials.

机译：统计可识别性和替代终点问题，应用于疫苗试验。

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摘要

Given a randomized treatment Z, a clinical outcome Y, and a biomarker S measured some fixed time after Z is administered, we may be interested in addressing the surrogate endpoint problem by evaluating whether S can be used to reliably predict the effect of Z on Y. Several recent proposals for the statistical evaluation of surrogate value have been based on the framework of principal stratification. In this article, we consider two principal stratification estimands: joint risks and marginal risks. Joint risks measure causal associations (CAs) of treatment effects on S and Y, providing insight into the surrogate value of the biomarker, but are not statistically identifiable from vaccine trial data. Although marginal risks do not measure CAs of treatment effects, they nevertheless provide guidance for future research, and we describe a data collection scheme and assumptions under which the marginal risks are statistically identifiable. We show how different sets of assumptions affect the identifiability of these estimands; in particular, we depart from previous work by considering the consequences of relaxing the assumption of no individual treatment effects on Y before S is measured. Based on algebraic relationships between joint and marginal risks, we propose a sensitivity analysis approach for assessment of surrogate value, and show that in many cases the surrogate value of a biomarker may be hard to establish, even when the sample size is large.
机译：给定随机治疗Z，临床结局Y和在施用Z之后一定时间测量的生物标志物S，我们可能有兴趣通过评估S是否可以可靠地预测Z对Y的影响来解决替代终点问题。最近的一些关于代理价值统计评估的建议是基于主体分层的框架。在本文中，我们考虑了两个主要的分层估计：共同风险和边际风险。联合风险度量了对S和Y的治疗效果的因果关联（CA），可提供对生物标志物替代价值的深入了解，但无法从疫苗试验数据中进行统计学鉴定。尽管边际风险不能衡量治疗效果的CA，但它们仍为将来的研究提供了指导，并且我们描述了一种数据收集方案和假设，在这些数据收集方案和假设下，边际风险在统计上是可识别的。我们展示了不同的假设如何影响这些估计的可识别性。特别是，我们通过考虑放宽在测量S之前不对Y进行单独处理的假设的后果而背离了先前的工作。基于共同风险和边际风险之间的代数关系，我们提出了一种用于评估替代价值的敏感性分析方法，并表明在许多情况下，即使样本量很大，生物标志物的替代价值也可能难以建立。

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