Clinical trials that evaluate the efficacy of a treatment on acquisition of or survival time from various diseases can be time consuming and expensive. Biostatisticians are interested in identifying predictors of the treatment effect on clinical trial outcomes (or “end points”) that occur much earlier and therefore could either shorten a trial or reveal evidence of potential efficacy. Recently, the term ‘principal surrogate’ has been proposed to account for the association between treatment effect on a biomarker and treatment effect on trial outcome. VIDD assistant member Dr. Ying Huang and member Dr. Peter Gilbert developed a novel summary measure that can be used for evaluating and comparing different surrogates for HIV vaccines.
The authors proposed a tool termed the standardized total gain (STG) to compare and contrast principal surrogates for a given clinical trial. The authors applied their method to data from the Step HIV trial, which evaluated the efficacy of the MRKAd5 HIV-1 Gag/Pol/Nef vaccine (versus placebo) on HIV disease diagnosis 3 years post-immunization. Among men, vaccine recipients had approximately a 2-fold higher risk of HIV acquisition than those receiving placebo and the current study set out to determine if the subject’s vaccine-induced T-cell response at 8 weeks post-randomization was a reliable predictor of the vaccine-induced increase in HIV risk. When analyzing the T-cell responses to each HIV antigen separately for surrogacy, Nef showed more variability in risk difference as compared to Gag and Pol, although no statistically significant difference was found between any two markers/antigens. As multiple surrogate biomarkers are used for predicting clinical outcome, it becomes more and more complicated to identify any one biomarker’s contribution. Therefore, the authors propose analyzing the net effect of a model that can include multiple markers. This type of measurement is crucial for choosing highly correlative principal surrogates that are in fact significant representatives of the treatment effect on clinical trial end points. – MDM
Huang Y, Gilbert PB. Comparing biomarkers as principal surrogate endpoints. Biometrics. 2011 Dec;67(4):1442-51.