Julian Wolfson
The identification and evaluation of surrogate endpoints is a major goal of many clinical studies. Pinpointing a reliable surrogate endpoint, often a biomarker, can help predict individual outcomes at an early stage, potentially shortening study length and cost. It may also suggest biological targets for new treatments. Though the search for useful surrogates is relevant to many different research areas, in this talk I will focus on surrogate endpoint assessment in the HIV vaccine context. In this domain, the aim is to identify vaccine-induced immunological responses which can be used as surrogates for HIV infection. A key feature of these immune responses is that they are generally measured some time after vaccination; for example, the T-cell response to a vaccine may take several weeks to fully develop. Analyses which treat such post-randomization measurements as baseline covariates are based on net effects, a mixture of the causal effect of treatment on the outcome and any systematic differences between the groups sharing similar values of the biomarker.
In this talk, I will introduce and explain the concept of principal stratification and apply it to isolate causal effects and assess surrogate value in this context. As with most techniques based on counterfactuals, there is a substantial amount of missing data which must be imputed. I will describe some assumptions and a novel vaccine trial design (due to Follmann) which help to identify Surrogate Risk (SR), the estimand of interest. Even with these assumptions and additional data, SR is a function of several non-identifiable terms, so I propose a sensitivity analysis approach to estimation. I will conclude with a discussion of a closely related estimand, Predictive Risk (PR), which is completely identifiable from observed data.
