Ran Tao, Ph.D., Department of Biostatistics, Vanderbilt University

"Design and Analysis of Two-Phase Studies, with Applications to Genetic Association Studies"

In modern epidemiological and medical studies, the covariates of interest may involve genome sequencing or biomarker assay 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 this information is used to select subjects for measurements of expensive covariates during the second phase. 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 and establish the consistency, asymptotic normality, and asymptotic efficiency of the estimators. In addition, we demonstrate the usefulness of the proposed methods through extensive simulation studies and applications to the National Heart, Lung, and Blood Institute (NHLBI) Exome Sequencing Project (ESP). Finally, we derive optimal two-phase designs, which can be substantially more efficient than the current designs.

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