title: Calibration weighted estimation of semiparametric transformation models for two-phase sampling.
abstract: Two-phase designs are commonly used to subsample subjects from a cohort in order to study covariates that are too expensive to ascertain for everyone in the cohort. This is particularly true for the study of immune response biomarkers in vaccine immunology, where new, elaborate assays are constantly being developed to improve our understanding of the human immune responses to vaccines and how the immune response may protect humans from virus infection. It has long being recognized that if there exist variables that are correlated with expensive variables and can be measured for every subject in the cohort, they can be leveraged to improve the estimation efficiency for the effects of the expensive variables. In this research article, we developed an improved inverse probability weighted estimation approach for semiparametric transformation models with a two-phase study design. Semiparametric transformation models are a class of models that include the Cox PH and proportional odds models. They provide an attractive way to model the effects of immune response biomarkers as human immune responses generally wane over time. Our approach is based on weights calibration, which has its origin in survey statistics and was used by Breslow et al. to improve inverse probability weighted estimation of the Cox regression model. We develop asymptotic theory for our estimator and examine its performance through simulation studies. We illustrate the proposed method with application to two HIV-1 vaccine efficacy trials.
authors: Vaccine and Infectious Disease Division and Public Health Sciences Division, Fred Hutchinson Cancer Research Center.
authors: Youyi
authors: Y
authors: Fong
authors: Peter
authors: P
authors: Gilbert
mesh_terms: D016915:AIDS Vaccines / Q000276:immunology*; D015982:Bias; D015415:Biomarkers; D002138:Calibration; D017326:Clinical Trials, Phase III as Topic / Q000379:methods / Q000706:statistics & numerical data*; D015331:Cohort Studies; D003198:Computer Simulation; D015658:HIV Infections / Q000276:immunology / Q000517:prevention & control* / Q000821:virology; D015497:HIV-1 / Q000276:immunology; D006801:Humans; D007111:Immunity, Cellular / Q000187:drug effects*; D053208:Kaplan-Meier Estimate; D009010:Monte Carlo Method; D011336:Probability; D016016:Proportional Hazards Models; D012107:Research Design
chemical_list: D016915:AIDS Vaccines; D015415:Biomarkers
keywords: Cox PH model; linear transformation models; proportional odds model; raking estimator; vaccine efficacy trial
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