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

机译:两阶段采样下的有效半参数推理及其在遗传关联研究中的应用

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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.
机译:在现代流行病学和临床研究中,感兴趣的协变量可能涉及基因组测序,生物标志物测定或医学成像,因此对大量受试者进行测量非常昂贵。一种经济有效的解决方案是两阶段设计,在该阶段下,可以在第一阶段观察所有受试者的结果和便宜的协变量,并使用该信息来选择受试者以在第二阶段测量昂贵的协变量。例如,在国家心脏,肺和血液研究所(NHLBI)外显子组测序项目(ESP)中,选择了具有极高定量特征值的受试者进行全外显子组测序。在这里,我们考虑一般的两阶段设计,其中结果可以是连续的或离散的,廉价的协变量可以是连续的并与昂贵的协变量相关。我们提出一种半参数方法,通过在给定廉价协变量与B样条筛子的情况下,对昂贵协变量的条件密度函数进行近似,来进行回归分析。我们设计了一种计算有效且数值稳定的EM算法,以最大化筛分的可能性。此外,我们建立了估计量的一致性,渐近正态性和渐近效率。此外,我们通过广泛的仿真研究证明了所提出的方法优于现有方法的优越性。最后,我们介绍了上述NHLBI ESP的应用程序。

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