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Wang Y, Ghassabian A, Gu B, Afanasyeva Y, Li Y, Trasande L, Liu M. Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures. Biometrics 2023; 79:2619-2632. [PMID: 35612351 PMCID: PMC10718172 DOI: 10.1111/biom.13702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 05/18/2022] [Indexed: 11/29/2022]
Abstract
Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).
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Affiliation(s)
- Yuyan Wang
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
| | - Akhgar Ghassabian
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
- Department of Pediatrics, NYU Grossman School of Medicine, New York, New York, USA
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Bo Gu
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
| | - Yelena Afanasyeva
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
| | - Yiwei Li
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
| | - Leonardo Trasande
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
- Department of Pediatrics, NYU Grossman School of Medicine, New York, New York, USA
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, New York, USA
- NYU Wagner School of Public Service, New York, New York, USA
- NYU School of Global Public Health, New York, New York, USA
| | - Mengling Liu
- Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA
- Department of Environmental Medicine, NYU Grossman School of Medicine, New York, New York, USA
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Xu Y, Kim I, Carroll RJ. A hybrid omnibus test for generalized semiparametric single-index models with high-dimensional covariate sets. Biometrics 2019; 75:757-767. [PMID: 30859553 DOI: 10.1111/biom.13054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Accepted: 02/26/2019] [Indexed: 11/27/2022]
Abstract
Numerous statistical methods have been developed for analyzing high-dimensional data. These methods often focus on variable selection approaches but are limited for the purpose of testing with high-dimensional data. They are often required to have explicit-likelihood functions. In this article, we propose a "hybrid omnibus test" for high-dicmensional data testing purpose with much weaker requirements. Our hybrid omnibus test is developed under a semiparametric framework where a likelihood function is no longer necessary. Our test is a version of a frequentist-Bayesian hybrid score-type test for a generalized partially linear single-index model, which has a link function being a function of a set of variables through a generalized partially linear single index. We propose an efficient score based on estimating equations, define local tests, and then construct our hybrid omnibus test using local tests. We compare our approach with an empirical-likelihood ratio test and Bayesian inference based on Bayes factors, using simulation studies. Our simulation results suggest that our approach outperforms the others, in terms of type I error, power, and computational cost in both the low- and high-dimensional cases. The advantage of our approach is demonstrated by applying it to genetic pathway data for type II diabetes mellitus.
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Affiliation(s)
- Yangyi Xu
- Department of Statistics, Virginia Tech., Blacksburg, Virginia
| | - Inyoung Kim
- Department of Statistics, Virginia Tech., Blacksburg, Virginia
| | - Raymond J Carroll
- Department of Statistics, Texas A&M University, 3143 TAMU, College Station, Texas.,School of Mathematical and Physical Sciences, University of Technology, Sydney, Sydney, Broadway, NSW, Australia
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Zhao X, Wang W, Liu L, Shih YCT. A flexible quantile regression model for medical costs with application to Medical Expenditure Panel Survey Study. Stat Med 2018; 37:2645-2666. [PMID: 29722044 DOI: 10.1002/sim.7670] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Revised: 03/03/2018] [Accepted: 03/08/2018] [Indexed: 11/11/2022]
Abstract
Medical costs are often skewed to the right and heteroscedastic, having a sophisticated relation with covariates. Mean function regression models with low-dimensional covariates have been extensively considered in the literature. However, it is important to develop a robust alternative to find the underlying relationship between medical costs and high-dimensional covariates. In this paper, we propose a new quantile regression model to analyze medical costs. We also consider variable selection, using an adaptive lasso penalized variable selection method to identify significant factors of the covariates. Simulation studies are conducted to illustrate the performance of the estimation method. We apply our method to the analysis of the Medical Expenditure Panel Survey dataset.
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Affiliation(s)
- Xiaobing Zhao
- School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, Zhejiang, China
| | - Weiwei Wang
- School of Statistics, East China Normal University, Shanghai, China
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, U.S.A
| | - Ya-Chen T Shih
- Department of Health Services Research, MD Anderson Cancer Center, Houston, TX, U.S.A
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