Comparing Biomarkers as Principal Surrogate Endpoints

Fred Hutchinson Cancer Research Center, Vaccine & Infectious Disease Division, Seattle, Washington 98109, USA.
Biometrics (Impact Factor: 1.52). 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.


Available from: Ying Huang, Mar 26, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Interrogating immune correlates of infection risk for efficacious and non-efficacious HIV-1 vaccine clinical trials have provided hypotheses regarding the mechanisms of induction of protective immunity to HIV-1. To date, there have been six HIV-1 vaccine efficacy trials (VAX003, Vaxgen, Inc., San Francisco, CA, USA), VAX004 (Vaxgen, Inc.), HIV-1 Vaccine Trials Network (HVTN) 502 (Step), HVTN 503 (Phambili), RV144 (sponsored by the U.S. Military HIV Research Program, MHRP) and HVTN 505). Cellular, humoral, host genetic and virus sieve analyses of these human clinical trials each can provide information that may point to potentially protective mechanisms for vaccine-induced immunity. Critical to staying on the path toward development of an efficacious vaccine is utilizing information from previous human and non-human primate studies in concert with new discoveries of basic HIV-1 host-virus interactions. One way that past discoveries from correlate analyses can lead to novel inventions or new pathways toward vaccine efficacy is to examine the intersections where different components of the correlate analyses overlap (e.g., virus sieve analysis combined with humoral correlates) that can point to mechanistic hypotheses. Additionally, differences in durability among vaccine-induced T- and B-cell responses indicate that time post-vaccination is an important variable. Thus, understanding the nature of protective responses, the degree to which such responses have, or have not, as yet, been induced by previous vaccine trials and the design of strategies to induce durable T- and B-cell responses are critical to the development of a protective HIV-1 vaccine.
    03/2014; 2(1):15-35. DOI:10.3390/vaccines2010015
  • [Show abstract] [Hide abstract]
    ABSTRACT: The Phase III Zostavax Efficacy and Safety Trial of 1 dose of licensed zoster vaccine (ZV) (Zostavax; Merck) in 50-59 year-olds showed approximately 70% vaccine efficacy (VE) to reduce the incidence of herpes zoster (HZ). An objective of the trial was to assess immune response biomarkers measuring antibodies to varicella zoster virus (VZV) [by glycoprotein enzyme-linked immunosorbent assay] as correlates of protection (CoPs) against HZ. The principal stratification 'vaccine efficacy curve' framework for statistically evaluating immune response biomarkers as CoPs was applied. The VE curve describes how VE against the clinical endpoint (HZ) varies across participant subgroups defined by biomarker readout measuring vaccine-induced immune response. The VE curve was estimated using several subgroup definitions. The fold rise in VZV antibody titers from pre-immunization to 6 weeks was an excellent CoP, with VE increasing sharply with fold rise [VE estimated at 0% for the subgroup with no rise and at 90% for the subgroup with 5.26-fold rise]. In contrast, VZV antibody titers measured 6 weeks after immunization did not predict VE, with similar estimated VE across titer subgroups. The analysis illustrates the value of the VE curve framework for assessing immune response biomarkers as CoPs in vaccine efficacy trials.
    The Journal of Infectious Diseases 05/2014; 210(10). DOI:10.1093/infdis/jiu279 · 5.78 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.
    Statistics in Medicine 10/2014; 34(3). DOI:10.1002/sim.6349 · 2.04 Impact Factor