1
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Lee M, Troxel AB, Liu M. Partial-linear single-index transformation models with censored data. LIFETIME DATA ANALYSIS 2024:10.1007/s10985-024-09624-z. [PMID: 38625444 DOI: 10.1007/s10985-024-09624-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 03/26/2024] [Indexed: 04/17/2024]
Abstract
In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients' mortality and a Veteran's Administration lung cancer trial.
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Affiliation(s)
- Myeonggyun Lee
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, 10016, USA.
| | - Andrea B Troxel
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, 10016, USA
| | - Mengling Liu
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, 10016, USA
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2
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Liang M, Yu M. Relative contrast estimation and inference for treatment recommendation. Biometrics 2023; 79:2920-2932. [PMID: 36645310 DOI: 10.1111/biom.13826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 12/29/2022] [Indexed: 01/17/2023]
Abstract
When there are resource constraints, it may be necessary to rank individualized treatment benefits to facilitate the prioritization of assigning different treatments. Most existing literature on individualized treatment rules targets absolute conditional treatment effect differences as a metric for the benefit. However, there can be settings where relative differences may better represent such benefit. In this paper, we consider modeling such relative differences formed as scale-invariant contrasts between the conditional treatment effects. By showing that all scale-invariant contrasts are monotonic transformations of each other, we posit a single index model for a particular relative contrast. We then characterize semiparametric estimating equations, including the efficient score, to estimate index parameters. To achieve semiparametric efficiency, we propose a two-step approach that minimizes a doubly robust loss function for initial estimation and then performs a one-step efficiency augmentation procedure. Careful theoretical and numerical studies are provided to show the superiority of our proposed approach.
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Affiliation(s)
- Muxuan Liang
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA
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3
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Chen Z, Wang S. Inferences for extended partially linear single-index models. TEST-SPAIN 2023. [DOI: 10.1007/s11749-022-00845-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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4
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Linlin G, Yang L. Statistical inference for the partially linear single-index model of panel data with serially correlated error structure. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2020.1860226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Gui Linlin
- Department of Finance, School of Economics and Management, Chongqing University, Chongqing, China
| | - Liu Yang
- Department of Statistics, School of Mathematics and Statistics, Chongqing University, Chongqing, China
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5
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Zhang J. Partial linear additive distortion measurement errors models. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2022.2076126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
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6
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Zheng Y, Zhao X, Zhang X. Quantile regression for massive data with network-induced dependence, and application to the New York statewide planning and research cooperative system. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2020.1786120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Yanqiao Zheng
- Department of Financial Engineering, School of Finance, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Xiaobing Zhao
- School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
| | - Xiaoqi Zhang
- Department of Financial Engineering, School of Finance, Zhejiang University of Finance and Economics, Hangzhou, China
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7
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Ding H, Zhang R, Zhu H. New estimation for heteroscedastic single-index measurement error models. J Nonparametr Stat 2022. [DOI: 10.1080/10485252.2021.2025238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hui Ding
- School of Economics, Nanjing University of Finance and Economics, Nanjing, People's Republic of China
| | - Riquan Zhang
- School of Statistics, Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, East China Normal University, Shanghai, People's Republic of China
| | - Hanbing Zhu
- School of Statistics, Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, East China Normal University, Shanghai, People's Republic of China
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8
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Kulasekera K, Siriwardhana C. Multi-Response Based Personalized Treatment Selection with Data from Crossover Designs for Multiple Treatments. COMMUN STAT-SIMUL C 2022; 51:554-569. [PMID: 35299995 PMCID: PMC8923529 DOI: 10.1080/03610918.2019.1656739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
In this work we propose a novel method for treatment selection based on individual covariate information when the treatment response is multivariate and data are available from a crossover design. Our method covers any number of treatments and it can be applied for a broad set of models. The proposed method uses a rank aggregation technique to estimate an ordering of treatments based on ranked lists of treatment performance measures such as smooth conditional means and conditional probability of a response for one treatment dominating others. An empirical study demonstrates the performance of the proposed method in finite samples.
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Affiliation(s)
- K.B. Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY 40202, USA
| | - Chathura Siriwardhana
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, HI 96813, USA
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9
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du Roy de Chaumaray M, Marbac M, Patilea V. Wilks’ theorem for semiparametric regressions with weakly dependent data. Ann Stat 2021. [DOI: 10.1214/21-aos2081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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10
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Novo S, Vieu P, Aneiros G. Fast and efficient algorithms for sparse semiparametric bifunctional regression. AUST NZ J STAT 2021. [DOI: 10.1111/anzs.12355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Silvia Novo
- Departamento de Matemáticas, Facultade de Informática Universidade da Coruña Campus de Elviña A Coruña 15071 Spain
| | - Philippe Vieu
- Institut de Mathématiques de Toulouse Université Paul Sabatier Route de Narbonne Toulouse OccitanieF‐31062, Cedex 9 France
| | - Germán Aneiros
- Departamento de Matemáticas, Facultade de Informática Universidade da Coruña Campus de Elviña A Coruña 15071 Spain
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11
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Feng Z, Zhang J, Yang B. Average derivation estimation with multiplicative distortion measurement errors. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1992635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Zhenghui Feng
- School of Economics, and Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China
| | - Jun Zhang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
| | - Baojun Yang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
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12
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Gai Y, Zhang J. Single-index relative error regression models. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1982972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Yujie Gai
- School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
| | - Jun Zhang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
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13
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Zhang J, Lin B. Estimation of correlation coefficient with general distortion measurement errors. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1963453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
| | - Bingqing Lin
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
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14
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Chen Z, Chen J. Bayesian analysis of partially linear, single-index, spatial autoregressive models. Comput Stat 2021. [DOI: 10.1007/s00180-021-01123-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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15
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Sparse semiparametric regression when predictors are mixture of functional and high-dimensional variables. TEST-SPAIN 2021. [DOI: 10.1007/s11749-020-00728-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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16
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Wu Y, Wang L, Fu H. Model-Assisted Uniformly Honest Inference for Optimal Treatment Regimes in High Dimension. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2021.1929246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Yunan Wu
- Yale University, Department of Biostatistics, New Haven, 06520 United States
| | - Lan Wang
- University of Miami, Department of Management Science, Coral Gables, 33124 United States
| | - Haoda Fu
- Eli Lilly and Company, Biometrics and Advanced Analytics, Indianapolis, United States
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17
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Wu S, Chen Y, Li Z, Li J, Zhao F, Su X. Towards multi-label classification: Next step of machine learning for microbiome research. Comput Struct Biotechnol J 2021; 19:2742-2749. [PMID: 34093989 PMCID: PMC8131981 DOI: 10.1016/j.csbj.2021.04.054] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/21/2021] [Accepted: 04/22/2021] [Indexed: 11/22/2022] Open
Abstract
Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features that highly correlated with the target diseases, so as to determine the status of new samples. To clearly understand the host-microbe interaction of specific diseases, previous studies always focused on well-designed cohorts, in which each sample was exactly labeled by a single status type. However, in fact an individual may be associated with multiple diseases simultaneously, which introduce additional variations on microbial patterns that interferes the status detection. More importantly, comorbidities or complications can be missed by regular ML models, limiting the practical application of microbiome techniques. In this review, we summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset. Then we prospect a further step of ML towards multi-label classification that potentially solves the aforementioned problem, including a series of promising strategies and key technical issues for applying multi-label classification in microbiome-based studies.
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Affiliation(s)
- Shunyao Wu
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Yuzhu Chen
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Zhiruo Li
- School of Mathematics and Statistics, Qingdao University, Qingdao, Shandong 266071, China
| | - Jian Li
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Fengyang Zhao
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
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18
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Zhang J. Model checking for multiplicative linear regression models with mixed estimators. STAT NEERL 2021. [DOI: 10.1111/stan.12239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics Shenzhen University Shenzhen China
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19
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Liang HY, Wang BH, Shen Y. Quantile regression of partially linear single-index model with missing observations. STATISTICS-ABINGDON 2021. [DOI: 10.1080/02331888.2021.1883613] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Han-Ying Liang
- School of Mathematical Sciences, Tongji University, Shanghai, People's Republic of China
| | - Bao-Hua Wang
- School of Mathematical Sciences, Tongji University, Shanghai, People's Republic of China
| | - Yu Shen
- School of Mathematical Sciences, Tongji University, Shanghai, People's Republic of China
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20
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Han C, Sun X, Gao W. Estimation and variable selection for a class of quantile regression models with multiple index. COMMUN STAT-THEOR M 2021. [DOI: 10.1080/03610926.2019.1633353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Cun Han
- School of Statistics, Shandong Technology and Business University, Yantai, China
| | - Xiaofei Sun
- School of Statistics, Shandong Technology and Business University, Yantai, China
| | - Wenliang Gao
- School of Statistics, Shandong Technology and Business University, Yantai, China
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21
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22
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Li T, Guo Y. Penalized profile quasi-maximum likelihood method of partially linear spatial autoregressive model. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1788561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Tizheng Li
- Department of Mathematics, Xi'an University of Architecture and Technology, Xi'an, People's Republic of China
| | - Yue Guo
- Department of Mathematics, Xi'an University of Architecture and Technology, Xi'an, People's Republic of China
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23
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Huang H, Shangguan J, Li X, Liang H. High-dimensional single-index models with censored responses. Stat Med 2020; 39:2743-2754. [PMID: 32379359 DOI: 10.1002/sim.8571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 04/04/2020] [Accepted: 04/15/2020] [Indexed: 11/09/2022]
Abstract
In this article, we study the estimation of high-dimensional single index models when the response variable is censored. We hybrid the estimation methods for high-dimensional single-index models (but without censorship) and univariate nonparametric models with randomly censored responses to estimate the index parameters and the link function and apply the proposed methods to analyze a genomic dataset from a study of diffuse large B-cell lymphoma. We evaluate the finite sample performance of the proposed procedures via simulation studies and establish large sample theories for the proposed estimators of the index parameter and the nonparametric link function under certain regularity conditions.
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Affiliation(s)
- Hailin Huang
- Department of Statistics, George Washington University, Washington, District of Columbia, USA
| | - Jizi Shangguan
- Department of Statistics, George Washington University, Washington, District of Columbia, USA
| | - Xinmin Li
- School of Mathematics and Statistics, Qingdao University, Shandong, China
| | - Hua Liang
- Department of Statistics, George Washington University, Washington, District of Columbia, USA
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24
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Wang Y, Wu Y, Jacobson MH, Lee M, Jin P, Trasande L, Liu M. A family of partial-linear single-index models for analyzing complex environmental exposures with continuous, categorical, time-to-event, and longitudinal health outcomes. Environ Health 2020; 19:96. [PMID: 32912175 PMCID: PMC7488560 DOI: 10.1186/s12940-020-00644-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 08/12/2020] [Indexed: 05/03/2023]
Abstract
BACKGROUND Statistical methods to study the joint effects of environmental factors are of great importance to understand the impact of correlated exposures that may act synergistically or antagonistically on health outcomes. This study proposes a family of statistical models under a unified partial-linear single-index (PLSI) modeling framework, to assess the joint effects of environmental factors for continuous, categorical, time-to-event, and longitudinal outcomes. All PLSI models consist of a linear combination of exposures into a single index for practical interpretability of relative direction and importance, and a nonparametric link function for modeling flexibility. METHODS We presented PLSI linear regression and PLSI quantile regression for continuous outcome, PLSI generalized linear regression for categorical outcome, PLSI proportional hazards model for time-to-event outcome, and PLSI mixed-effects model for longitudinal outcome. These models were demonstrated using a dataset of 800 subjects from NHANES 2003-2004 survey including 8 environmental factors. Serum triglyceride concentration was analyzed as a continuous outcome and then dichotomized as a binary outcome. Simulations were conducted to demonstrate the PLSI proportional hazards model and PLSI mixed-effects model. The performance of PLSI models was compared with their counterpart parametric models. RESULTS PLSI linear, quantile, and logistic regressions showed similar results that the 8 environmental factors had both positive and negative associations with triglycerides, with a-Tocopherol having the most positive and trans-b-carotene having the most negative association. For the time-to-event and longitudinal settings, simulations showed that PLSI models could correctly identify directions and relative importance for the 8 environmental factors. Compared with parametric models, PLSI models got similar results when the link function was close to linear, but clearly outperformed in simulations with nonlinear effects. CONCLUSIONS We presented a unified family of PLSI models to assess the joint effects of exposures on four commonly-used types of outcomes in environmental research, and demonstrated their modeling flexibility and effectiveness, especially for studying environmental factors with mixed directional effects and/or nonlinear effects. Our study has expanded the analytical toolbox for investigating the complex effects of environmental factors. A practical contribution also included a coherent algorithm for all proposed PLSI models with R codes available.
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Affiliation(s)
- Yuyan Wang
- Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY 10016 USA
| | - Yinxiang Wu
- Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY 10016 USA
| | | | - Myeonggyun Lee
- Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY 10016 USA
| | - Peng Jin
- Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY 10016 USA
| | - Leonardo Trasande
- Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY 10016 USA
- Department of Pediatrics, NYU Langone Health, New York, NY USA
- Department of Environmental Medicine, NYU Langone Health, New York, NY USA
| | - Mengling Liu
- Department of Population Health, NYU Langone Health, 180 Madison Avenue, New York, NY 10016 USA
- Department of Environmental Medicine, NYU Langone Health, New York, NY USA
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25
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Feng Y, Xiao L, Chi EC. Sparse Single Index Models for Multivariate Responses. J Comput Graph Stat 2020; 30:115-124. [PMID: 34025100 DOI: 10.1080/10618600.2020.1779080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Joint models are popular for analyzing data with multivariate responses. We propose a sparse multivariate single index model, where responses and predictors are linked by unspecified smooth functions and multiple matrix level penalties are employed to select predictors and induce low-rank structures across responses. An alternating direction method of multipliers (ADMM) based algorithm is proposed for model estimation. We demonstrate the effectiveness of proposed model in simulation studies and an application to a genetic association study.
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Affiliation(s)
- Yuan Feng
- Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203
| | - Luo Xiao
- Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203
| | - Eric C Chi
- Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203
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26
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Li T, Yin Q, Peng J. Variable selection of partially linear varying coefficient spatial autoregressive model. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1788560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Tizheng Li
- Department of Mathematics, School of Science, Xi'an University of Architecture and Technology, Xi'an, People's Republic of China
| | - Qingyan Yin
- Department of Mathematics, School of Science, Xi'an University of Architecture and Technology, Xi'an, People's Republic of China
| | - Jialong Peng
- Department of Mathematics, School of Science, Xi'an University of Architecture and Technology, Xi'an, People's Republic of China
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27
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Zhang J, Feng S, Gai Y. Partial index additive models with additive distortion measurement errors. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2020.1757712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
| | - Sanying Feng
- School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, China
| | - Yujie Gai
- School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
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28
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Gai Y, Zhang J. Detection of the symmetry of model errors for partial linear single-index models. COMMUN STAT-SIMUL C 2020. [DOI: 10.1080/03610918.2020.1752381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Yujie Gai
- School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
| | - Jun Zhang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, China
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29
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Zhang J, Yang Y, Feng S, Wei Z. Logarithmic calibration for partial linear models with multiplicative distortion measurement errors. J STAT COMPUT SIM 2020. [DOI: 10.1080/00949655.2020.1750614] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, People's Republic of China
| | - Yiping Yang
- College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, People's Republic of China
| | - Sanying Feng
- School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, People's Republic of China
| | - Zhenghong Wei
- College of Mathematics and Statistics, Shenzhen University, Shenzhen, People's Republic of China
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30
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Zou Y, Fan G, Zhang R. Empirical likelihood and variable selection for partially linear single-index EV models with missing censoring indicators. J Korean Stat Soc 2020. [DOI: 10.1007/s42952-020-00065-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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31
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Lian H. Asymptotics of the Non‐parametric Function for B‐splines‐based Estimation in Partially Linear Models. Int Stat Rev 2020. [DOI: 10.1111/insr.12346] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Heng Lian
- Department of MathematicsCity University of Hong Kong Kowloon Tong Hong Kong
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32
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Cai L, Jin L, Wang S. Oracally efficient estimation and simultaneous inference in partially linear single-index models for longitudinal data. Electron J Stat 2020. [DOI: 10.1214/20-ejs1723] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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33
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34
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Quantile regression and variable selection for partially linear single-index models with missing censoring indicators. J Stat Plan Inference 2020. [DOI: 10.1016/j.jspi.2019.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Zhang J, Lin B, Feng Z. Conditional absolute mean calibration for partial linear multiplicative distortion measurement errors models. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.06.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Siriwardhana C, Kulasekera KB, Datta S. Personalized treatment selection using data from crossover designs with carry-over effects. Stat Med 2019; 38:5391-5412. [PMID: 31637762 DOI: 10.1002/sim.8372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 05/29/2019] [Accepted: 08/24/2019] [Indexed: 11/07/2022]
Abstract
In this work, we propose a semiparametric method for estimating the optimal treatment for a given patient based on individual covariate information for that patient when data from a crossover design are available. Here, we assume there are carry-over effects for patients switching from one treatment to another. For the K treatment (K ≥ 2) scenario, we show that nonparametric estimation of carry-over effects can have the undesirable property that comparison of treatment means can only be done using independent outcome measurements from different groups of patients rather than using available joint measurements for each patient. To overcome this barrier, we compare probabilities of outcome variable of each treatment dominating outcome variables for all other treatments conditional on patient-specific scores constructed from patient covariates. We suggest single-index models as appropriate models connecting outcome variables to covariates and our empirical investigations show that frequencies of correct treatment assignments are highly accurate. The proposed method is also rather robust against departures from a single-index model structure. We also conduct a real data analysis to show the applicability of the proposed procedure.
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Affiliation(s)
- Chathura Siriwardhana
- Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii
| | - K B Kulasekera
- Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, Kentucky
| | - Somnath Datta
- Department of Biostatistics, University of Florida, Gainesville, Florida
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38
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Huang H, Shangguan J, Ruan P, Liang H. Bi-level feature selection in high dimensional AFT models with applications to a genomic study. Stat Appl Genet Mol Biol 2019; 18:/j/sagmb.ahead-of-print/sagmb-2019-0016/sagmb-2019-0016.xml. [PMID: 31525158 DOI: 10.1515/sagmb-2019-0016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.
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Affiliation(s)
- Hailin Huang
- Department of Statistics, George Washington University, Washington, DC 20052, USA
| | - Jizi Shangguan
- Department of Statistics, George Washington University, Washington, DC 20052, USA
| | - Peifeng Ruan
- Department of Statistics, George Washington University, Washington, DC 20052, USA
| | - Hua Liang
- Department of Statistics, George Washington University, Washington, DC 20052, USA
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39
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Estimation and variable selection for partial linear single-index distortion measurement errors models. Stat Pap (Berl) 2019. [DOI: 10.1007/s00362-019-01119-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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40
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Cai Q, Wang S. Inferences with generalized partially linear single-index models for longitudinal data. J Stat Plan Inference 2019. [DOI: 10.1016/j.jspi.2018.09.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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41
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Huang Z, Zhao X. Statistical estimation for a partially linear single-index model with errors in all variables. COMMUN STAT-THEOR M 2019. [DOI: 10.1080/03610926.2018.1425446] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Zhensheng Huang
- School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, P. R. China
| | - Xin Zhao
- School of Science, Nanjing University of Science and Technology, Nanjing, Jiangsu, P. R. China
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42
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Zhang J, Gai Y, Lin B, Zhu X. Nonlinear regression models with single‐index heteroscedasticity. STAT NEERL 2019. [DOI: 10.1111/stan.12170] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics, Institute of Statistical SciencesShenzhen University Shenzhen China
| | - Yujie Gai
- School of Statistics and MathematicsCentral University of Finance and Economics Beijing China
| | - Bingqing Lin
- College of Mathematics and Statistics, Institute of Statistical SciencesShenzhen University Shenzhen China
| | - Xuehu Zhu
- School of Mathematics and StatisticsXi'an Jiaotong University Xi'an China
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43
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Zhang J, Gai Y, Lin B. Detection of marginal heteroscedasticity for partial linear single-index models. COMMUN STAT-SIMUL C 2019. [DOI: 10.1080/03610918.2019.1565585] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China
| | - Yujie Gai
- School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
- Department of Biostatistics School of Public Health, University of Texas at Houston, Houston, TX, USA
| | - Bingqing Lin
- College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China
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Zhang J, Niu C, Li G. Exploring the constant coefficient of a single-index variation. BRAZ J PROBAB STAT 2019. [DOI: 10.1214/17-bjps377] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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46
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47
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Yu Y, Zou Z, Wang S. Statistical regression modeling for energy consumption in wastewater treatment. J Environ Sci (China) 2019; 75:201-208. [PMID: 30473285 DOI: 10.1016/j.jes.2018.03.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 03/19/2018] [Accepted: 03/20/2018] [Indexed: 06/09/2023]
Abstract
Wastewater treatment is one of critical issues faced by water utilities, and receives more and more attentions recently. The energy consumption modeling in biochemical wastewater treatment was investigated in the study via a general and robust approach based on Bayesian semi-parametric quantile regression. The dataset was derived from a municipal wastewater treatment plant, where the energy consumption of unit chemical oxygen demand (COD) reduction was the response variable of interest. Via the proposed approach, the comprehensive regression pictures of the energy consumption and truly influencing factors, i.e., the regression relationships at lower, median and higher energy consumption levels were characterized respectively. Meanwhile, the proposals for energy saving in different cases were also facilitated specifically. First, the lower level of energy consumption was closely associated with the temperature of influent wastewater, and the chroma-rich wastewater also showed helpful in the execution of energy saving. Second, at median energy consumption level, the COD-rich wastewater played a determinative role in the reduction of energy consumption, while the higher quality of treated water led to slightly energy intensive. Third, the higher level of energy consumption was most likely to be attributed to the relatively high temperature of wastewater and total nitrogen (TN)-rich wastewater, and both of the factors were preferably to be avoided to alleviate the burden of energy consumption. The study provided an efficient approach to controlling the energy consumption of wastewater treatment in the perspective of statistical regression modeling, and offered valuable suggestions for the future energy saving.
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Affiliation(s)
- Yang Yu
- School of Economics and Management, Beihang University, Beijing 100191, China
| | - Zhihong Zou
- School of Economics and Management, Beihang University, Beijing 100191, China.
| | - Shanshan Wang
- School of Economics and Management, Beihang University, Beijing 100191, China; Beijing Key Laboratory of Emergence Support Simulation Technologies for City Operations, Beijing 100191, China.
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Zhang J, Niu C, Lu T, Wei Z. Estimation of the error distribution function for partial linear single-index models. COMMUN STAT-SIMUL C 2018. [DOI: 10.1080/03610918.2018.1468461] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Jun Zhang
- College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China
| | - Cuizhen Niu
- School of Statistics, Beijing Normal University, Beijing, China
| | - Tao Lu
- Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA
| | - Zhenghong Wei
- College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China
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49
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Zhang J, Feng Z, Peng H. Estimation and hypothesis test for partial linear multiplicative models. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.06.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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50
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Statistical inference for linear regression models with additive distortion measurement errors. Stat Pap (Berl) 2018. [DOI: 10.1007/s00362-018-1057-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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