Evaluating a surrogate endpoint at three levels, with application to vaccine development.

Publication Type:

Journal Article


Statistics in medicine, Volume 27, Issue 23, p.4758-78 (2008)


2008, AIDS Vaccines, Algorithms, Biological Markers, Center-Authored Paper, Clinical Trials as Topic, Humans, Immunotherapy, Active, Influenza Vaccines, Meta-Analysis as Topic, Public Health Sciences Division, Research Design, Treatment Outcome, Vaccine and Infectious Disease Institute


Identification of an immune response to vaccination that reliably predicts protection from clinically significant infection, i.e. an immunological surrogate endpoint, is a primary goal of vaccine research. Using this problem of evaluating an immunological surrogate as an illustration, we describe a hierarchy of three criteria for a valid surrogate endpoint and statistical analysis frameworks for evaluating them. Based on a placebo-controlled vaccine efficacy trial, the first level entails assessing the correlation of an immune response with a study endpoint in the study groups, and the second level entails evaluating an immune response as a surrogate for the study endpoint that can be used for predicting vaccine efficacy for a setting similar to that of the vaccine trial. We show that baseline covariates, innovative study design, and a potential outcomes formulation can be helpful for this assessment. The third level entails validation of a surrogate endpoint via meta-analysis, where the goal is to evaluate how well the immune response can be used to predict vaccine efficacy for new settings (building bridges). A simulated vaccine trial and two example vaccine trials are presented, one supporting that certain anti-influenza antibody levels are an excellent surrogate for influenza illness and another supporting that certain anti-HIV antibody levels are not useful as a surrogate for HIV infection.