Date: 2012-03-30

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Statisticians have developed a number of frameworks which can be used to assess the surrogate value of a biomarker, i.e. establish whether treatment effects on a biological quantity measured shortly after administration of treatment predict treatment effects on the clinical endpoint of interest. The most commonly applied of these frameworks is due to Prentice (1989), who proposed a set of criteria which a surrogate marker should satisfy. However, verifying these criteria using observed data can be challenging due to the presence of unmeasured simultaneous predictors (i.e. confounders) which influence both the potential surrogate and the outcome. In this work, we adapt a technique proposed by Rosenbaum (2002) for observational studies, in which observations are matched and the odds of treatment within each matched pair is bounded. This yields a straightforward and interpretable sensitivity analysis which can be performed particularly efficiently for certain types of test statistics. In this talk, I will introduce the surrogate endpoint problem, discuss the details of my proposed technique for assessing surrogate value, and illustrate with some simulated examples inspired by the problem of identifying immune surrogates in HIV vaccine trials.

Speaker

Julian Wolfson is an Assistant Professor in the Division of Biostatistics at the University of Minnesota School of Public Health.