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Calibration weighted estimation of semiparametric transformation models for two‐phase sampling
Author(s) -
Fong Youyi,
Gilbert Peter
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6439
Subject(s) - estimator , covariate , inverse probability weighting , semiparametric regression , inverse probability , proportional hazards model , statistics , semiparametric model , computer science , econometrics , multivariate statistics , estimating equations , transformation (genetics) , calibration , vaccine trial , mathematics , medicine , human immunodeficiency virus (hiv) , immunology , biology , bayesian probability , biochemistry , posterior probability , gene
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 . [1][Breslow NE, 2009], [2][Breslow NE, 2009] 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. Copyright © 2015 John Wiley & Sons, Ltd.