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

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  • Ran Tao
  • Donglin Zeng
  • Dan-Yu Lin

Abstract

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

Suggested Citation

  • Ran Tao & Donglin Zeng & Dan-Yu Lin, 2017. "Efficient Semiparametric Inference Under Two-Phase Sampling, With Applications to Genetic Association Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1468-1476, October.
  • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:520:p:1468-1476
    DOI: 10.1080/01621459.2017.1295864
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    Citations

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    Cited by:

    1. Brady Ryan & Ananthika Nirmalkanna & Candemir Cigsar & Yildiz E. Yilmaz, 2023. "Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 510-539, July.
    2. Jacob M. Maronge & Ran Tao & Jonathan S. Schildcrout & Paul J. Rathouz, 2023. "Generalized case‐control sampling under generalized linear models," Biometrics, The International Biometric Society, vol. 79(1), pages 332-343, March.
    3. Sarah C. Lotspeich & Bryan E. Shepherd & Gustavo G. C. Amorim & Pamela A. Shaw & Ran Tao, 2022. "Efficient odds ratio estimation under two‐phase sampling using error‐prone data from a multi‐national HIV research cohort," Biometrics, The International Biometric Society, vol. 78(4), pages 1674-1685, December.
    4. Chiara Di Gravio & Ran Tao & Jonathan S. Schildcrout, 2023. "Design and analysis of two‐phase studies with multivariate longitudinal data," Biometrics, The International Biometric Society, vol. 79(2), pages 1420-1432, June.
    5. Gustavo Amorim & Ran Tao & Sarah Lotspeich & Pamela A. Shaw & Thomas Lumley & Bryan E. Shepherd, 2021. "Two‐phase sampling designs for data validation in settings with covariate measurement error and continuous outcome," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1368-1389, October.

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