title: Likelihood-based methods for evaluating principal surrogacy in augmented vaccine trials.
abstract: There is growing interest in assessing immune biomarkers, which are quick to measure and potentially predictive of long-term efficacy, as surrogate endpoints in randomized, placebo-controlled vaccine trials. This can be done under a principal stratification approach, with principal strata defined using a subject's potential immune responses to vaccine and placebo (the latter may be assumed to be zero). In this context, principal surrogacy refers to the extent to which vaccine efficacy varies across principal strata. Because a placebo recipient's potential immune response to vaccine is unobserved in a standard vaccine trial, augmented vaccine trials have been proposed to produce the information needed to evaluate principal surrogacy. This article reviews existing methods based on an estimated likelihood and a pseudo-score (PS) and proposes two new methods based on a semiparametric likelihood (SL) and a pseudo-likelihood (PL), for analyzing augmented vaccine trials. Unlike the PS method, the SL method does not require a model for missingness, which can be advantageous when immune response data are missing by happenstance. The SL method is shown to be asymptotically efficient, and it performs similarly to the PS and PL methods in simulation experiments. The PL method appears to have a computational advantage over the PS and SL methods.
authors.affiliation: 1 Department of Mathematics, Harbin Institute of Technology, Harbin, P. R. China.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.affiliation: 2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.affiliation: 3 Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: 2 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D015415:Biomarkers; D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D014612:Vaccines / Q000276:immunology / Q000494:pharmacology*
chemical_list: D015415:Biomarkers; D014612:Vaccines
keywords: Counterfactual; missing at random; missing covariate; potential outcome; pseudo-likelihood; semiparametric likelihood; surrogate endpoint