Article

Comparing Biomarkers as Principal Surrogate Endpoints

Fred Hutchinson Cancer Research Center, Vaccine & Infectious Disease Division, Seattle, Washington 98109, USA.
Biometrics (Impact Factor: 1.57). 04/2011; 67(4):1442-51. DOI: 10.1111/j.1541-0420.2011.01603.x
Source: PubMed

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. 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.

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Available from: Ying Huang, Mar 26, 2015
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    • "Without the exclusion restriction assumption, Zhang et al. (2009) used Gaussian mixture models to identify causal effects within principal strata. Gilbert and Hudgens (2008) and Huang and Gilbert (2011) proposed approaches to evaluating surrogates based on principal stratification in a single trial, but they assumed constant potential outcomes of the surrogate under control. Zigler and Belin (2012) proposed a Bayesian approach to estimating the causal effects on the endpoint within principal strata without the monotonicity assumption, but their approach relied on prior distributions on some parameters that are not identifiable. "
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    • "Without the exclusion restriction assumption, Zhang et al. (2009) used Gaussian mixture models to identify causal effects within principal strata. Gilbert and Hudgens (2008) and Huang and Gilbert (2011) proposed approaches to evaluating surrogates based on principal stratification in a single trial, but they assumed constant potential outcomes of the surrogate under control. Zigler and Belin (2012) proposed a Bayesian approach to estimating the causal effects on the endpoint within principal strata without the monotonicity assumption, but their approach relied on prior distributions on some parameters that are not identifiable. "
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