Comparing Biomarkers as Principal Surrogate Endpoints
Article first published online: 22 APR 2011
© 2011, The International Biometric Society
Volume 67, Issue 4, pages 1442–1451, December 2011
How to Cite
Huang, Y. and Gilbert, P. B. (2011), Comparing Biomarkers as Principal Surrogate Endpoints. Biometrics, 67: 1442–1451. doi: 10.1111/j.1541-0420.2011.01603.x
- Issue published online: 14 DEC 2011
- Article first published online: 22 APR 2011
- Received October 2009. Revised February 2011. Accepted March 2011.
- Estimated likelihood;
- Predictiveness curve;
- Principal stratification;
- Surrogate marker;
- Total gain
Summary 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. Despite its appealing interpretation, limited research has been conducted to evaluate principal surrogates, and existing methods focus on risk models that consider a single biomarker. 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. In addition, 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. This method accommodates two-phase sampling of biomarkers and is more widely applicable than existing nonparametric methods by incorporating continuous baseline covariates to predict the biomarker(s), and is more robust than existing parametric methods by leaving the error distribution of markers unspecified. The methodology is illustrated using a simulated example set and a real data set in the context of HIV vaccine trials.