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

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...

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Veröffentlicht in:	Journal of the American Statistical Association Jg. 112; H. 520; S. 1468 - 1476
Hauptverfasser:	Tao, Ran, Zeng, Donglin, Lin, Dan-Yu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 02.10.2017 Taylor & Francis Group,LLC Taylor & Francis Ltd
Schlagworte:	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 English for special purposes Epidemiology Extreme values Gene sequencing Genetics Genome sequencing Genomics image analysis Inference 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
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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
AbstractList	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 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) Exorne 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. 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. 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.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.
Author	Zeng, Donglin Tao, Ran Lin, Dan-Yu
Author_xml	– sequence: 1 givenname: Ran surname: Tao fullname: Tao, Ran organization: Department of Biostatistics, Vanderbilt University Medical Center – sequence: 2 givenname: Donglin surname: Zeng fullname: Zeng, Donglin organization: Department of Biostatistics, University of North Carolina – sequence: 3 givenname: Dan-Yu surname: Lin fullname: Lin, Dan-Yu email: lin@bios.unc.edu organization: Department of Biostatistics, Vanderbilt University Medical Center
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Cites_doi	10.1111/1467-9868.00185 10.1093/biomet/84.1.57 10.1080/01621459.2015.1008099 10.1198/016214504000001853 10.1016/S0197-2456(97)00078-0 10.1214/aos/1059655907 10.1016/1047-2797(91)90005-W 10.1093/biomet/asn073 10.1093/aje/kwf113 10.1016/0895-4356(88)90080-7 10.1093/oxfordjournals.aje.a115184 10.1210/jc.2011-0322 10.1016/S0140-6736(08)60208-1 10.1111/j.2517-6161.1995.tb02036.x 10.1093/biomet/92.2.399 10.1073/pnas.1221713110 10.1093/oxfordjournals.aje.a113266 10.1137/1.9781611973907 10.1198/016214503388619184 10.1007/s10985-007-9066-9 10.2105/AJPH.41.3.279 10.1038/ng.271
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SubjectTerms	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 English for special purposes Epidemiology Extreme values Gene sequencing Genetics Genome sequencing Genomics image analysis Inference 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
Title	Efficient Semiparametric Inference Under Two-Phase Sampling, With Applications to Genetic Association Studies
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