Comparing Biomarkers as Principal Surrogate Endpoints

• Activity Number: 664
• Type: Topic Contributed
• Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Ying Huang*+
• Companies: Columbia University
• Keywords: Estimated likelihood ; predictiveness curve ; principal stratification ; semiparametric ; surrogate marker ; total gain

Abstract

Recently a new definition of surrogate endpoint, the `principal surrogate', was proposed based on causal associations between treatment effects on the biomarker and on the clinical endpoint. How to compare principal surrogate value of biomarkers or general risk models that consider multiple biomarkers remains an open research question. We propose to characterize a marker or risk model's principal surrogate value based on the distribution of risk difference between interventions. And we propose a novel summary measure (the standardized total gain) that can be used to compare markers and to assess the incremental value of a new marker. We develop a semiparametric estimated-likelihood method to estimate the joint surrogate value of multiple biomarkers. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials.