A0330
Title: An optimal two-step estimation approach for two-phase studies
Authors: Kin Yau Wong - Hong Kong Polytechnic University (Hong Kong) [presenting]
Qingning Zhou - University of North Carolina at Charlotte (United States)
Abstract: Two-phase sampling is commonly adopted to reduce costs and improve estimation efficiency. The two-phase study design is considered where the outcome and some cheap covariates are observed for a large cohort in Phase I, and expensive covariates are obtained for a selected subset of the cohort in Phase II. As a result, the analysis of the association between the outcome and covariates faces a missing data problem. The complete case analysis that uses only the Phase II sample is generally inefficient. A two-step estimation approach is developed, which first obtains an estimator using the complete data and then updates it using an asymptotically mean-zero estimator obtained from a working model between the outcome and cheap covariates using the full data. The two-step estimator is asymptotically at least as efficient as the complete-data estimator and is robust to misspecification of the working model. A kernel-based method is proposed to construct a two-step estimator that achieves optimal efficiency, and also develop a simple joint update approach based on multiple working models to approximate the optimal estimator. The proposed method is based on the influence function and is generally applicable as long as the complete-data estimator is asymptotically linear. The advantages of the proposed method are demonstrated over the existing approaches via simulation studies and provide applications to real biomedical studies.
