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

• Published in: Journal of the American Statistical Association Vol. 112; no. 520; pp. 1468 - 1476
• Main Authors: Tao, Ran, Zeng, Donglin, Lin, Dan-Yu
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 02.10.2017; Taylor & Francis Group,LLC; Taylor & Francis Ltd
• Subjects: Algorithms; Applications and Case Studies; Asymptotic properties; Biased sampling; Biological markers; Biomarkers; Blood; clinical trials; Computer simulation; Cost analysis; cost effectiveness; Density; Dominance; EM algorithm; Epidemiology; Extreme values; Gene sequencing; Genome sequencing; Genomics; image analysis; Medical imaging; Normality; quantitative traits; Regression analysis; Response-selective sampling; Sampling; Semiparametric efficiency; sequence analysis; Sieve approximation; Simulation; simulation models; statistical inference; Statistical methods; Statistics; Semiparametric efficiency; Responseselective sampling; Biased sampling; Genome sequencing; EM algorithm; Sieve approximation
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1080/01621459.2017.1295864

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