Evaluating Causal Effect Predictiveness of Candidate Surrogate Endpoints

Frangakis and Rubin (2002, Biometrics) proposed a new definition of a surrogate endpoint (a “principal” surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. While the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate that predicts the biomarker. Given case-cohort sampling of such a baseline predictor and the biomarker in a single large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the “surrogate value” of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection.