Efficient Semiparametric Inference Under Two-Phase Sampling, With Applications to Genetic Association Studies

作     者：Tao, Ran Zeng, Donglin Lin, Dan-Yu 

作者机构：Vanderbilt Univ Med Ctr Dept Biostat Nashville TN USA Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA 

出 版 物：《JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION》 (美国统计学会志)

年 卷 期：2017年第112卷第520期

页      面：1468-1476页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：National Institute of Health [R01CA082659  R01GM047845  P01CA142538] 

主　　题：Biased sampling EM algorithm Genome sequencing Response-selective sampling Semiparametric efficiency Sieve approximation 

摘      要：In modern epidemiological and clinical studies, the covariates of interest may involve genome sequencing, biomarker assay, or medical imaging and thus are prohibitively expensive to measure on a large number of subjects. A cost-effective solution is the two-phase design, under which the outcome and inexpensive covariates are observed for all subjects during the first phase and that information is used to select subjects for measurements of expensive covariates during the second phase. For example, subjects with extreme values of quantitative traits were selected for whole-exome sequencing in the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP). Herein, we consider general two-phase designs, where the outcome can be continuous or discrete, and inexpensive covariates can be continuous and correlated with expensive covariates. We propose a semiparametric approach to regression analysis by approximating the conditional density functions of expensive covariates given inexpensive covariates with B-spline sieves. We devise a computationally efficient and numerically stable EM-algorithm to maximize the sieve likelihood. In addition, we establish the consistency, asymptotic normality, and asymptotic efficiency of the estimators. Furthermore, we demonstrate the superiority of the proposed methods over existing ones through extensive simulation studies. Finally, we present applications to the aforementioned NHLBI ESP. Supplementary materials for this article are available online