Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials
Statistical Identifiability and the Surrogate Endpoint Problem, with Application to Vaccine Trials
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DOI：
10.1111/j.1541-0420.2009.01380.x
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发表时间：
2010-12-01
期刊：
影响因子：
1.9
通讯作者：
Gilbert, Peter
中科院分区：
文献类型：
--
作者：
Wolfson, Julian;Gilbert, Peter
在给定随机治疗Z、临床结果Y和生物标记物S在应用Z后的某个固定时间测量的情况下，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。最近几个关于替代值的统计评估的建议都是基于主要分层的框架。在本文中，我们考虑了两种主要的分层标准：共同风险和边际风险。联合风险衡量S和Y的治疗效果的因果关系(CA)，提供对生物标记物的替代价值的洞察，但无法从疫苗试验数据中进行统计识别。虽然边际风险没有衡量治疗效果的CA，但它们为未来的研究提供了指导，我们描述了一个数据收集方案和假设，在这些方案和假设下，边际风险是统计上可识别的。我们展示了不同的假设集合如何影响这些估计的可识别性；特别是，我们通过考虑在S被测量之前放松对Y没有个体处理效应的假设的后果来偏离先前的工作。基于联合风险和边际风险之间的代数关系，我们提出了一种评估替代价值的灵敏度分析方法，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很难确定。
P>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.