Statistical identifiability and the surrogate endpoint problem, with application to vaccine trials.

ABSTRACT: 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 g

SUBMITTER: Wolfson J 

PROVIDER: S-EPMC3597127 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature