Evaluating candidate principal surrogate endpoints.
Frangakis and Rubin (2002, Biometrics 58, 21-29) 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. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case-cohort sampling of such a baseline predictor and the biomarker in a 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.
Gilbert
Peter B
PB
Department of Biostatistics, Fred Hutchinson Cancer Research Center, University of Washington, Seattle, Washington 98109, USA. pgilbert@scharp.org
Hudgens
Michael G
MG
D016915:AIDS Vaccines; D015415:Biomarkers; D001699:Biometry / Q000379:methods*; D023381:Endpoint Determination; D006801:Humans; D007109:Immunity; D007239:Infections / Q000628:therapy; D016013:Likelihood Functions; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic; D016896:Treatment Outcome
D016915:AIDS Vaccines; D015415:Biomarkers