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Novacek DM, Wynn JK, McCleery A, Reavis EA, Senturk D, Sugar CA, Tsai J, Green MF. Sustained mental health and functional responses to the COVID-19 pandemic in Black and White Veterans with psychosis or recent homelessness. J Psychiatr Res 2024; 172:102-107. [PMID: 38373371 DOI: 10.1016/j.jpsychires.2024.02.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/21/2024]
Abstract
The COVID-19 pandemic disproportionately impacted marginalized populations including Black Americans, people with serious mental illness, and individuals experiencing homelessness. Although the double disadvantage hypothesis would suggest that individuals with multiple minoritized statuses would experience worse psychosocial impacts from the pandemic, this may not be the case for vulnerable Black Veterans. The present study investigated the sustained mental health and functional responses to the pandemic in Black and White Veterans with psychosis or recent homelessness and in a control group of Veterans enrolled in the Department of Veterans Affairs healthcare services. Clinical interviews and questionnaires were administered remotely by telephone at five time points from May 2020 through July 2021, including a retrospective time point for March 2020 (i.e., before the pandemic started). Overall, there was a striking absence of systematic differences by race in the trajectories of psychiatric symptoms and functioning among Veterans during the study period. These findings are consistent with a report on initial responses to the pandemic that revealed only a few select differences by race among Veteran groups. The lack of racial disparities is inconsistent with the double disadvantage hypothesis. Although further investigation is needed, one possible interpretation is that the wrap-around services offered by the Veterans Health Administration may have mitigated expected differences by race among Veterans with psychosis or homelessness. Future research should continue to examine whether VA services mitigate disparities in mental health and psychosocial outcomes.
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Affiliation(s)
- Derek M Novacek
- Center on Enhancement of Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA.
| | - Jonathan K Wynn
- Center on Enhancement of Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Amanda McCleery
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA; Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA
| | - Eric A Reavis
- Center on Enhancement of Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
| | - Damla Senturk
- Department of Biostatistics, University of California, Los Angeles, CA, USA
| | - Catherine A Sugar
- Center on Enhancement of Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA; Department of Biostatistics, University of California, Los Angeles, CA, USA
| | - Jack Tsai
- School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA; National Center on Homelessness Among Veterans, US Department of Veterans Affairs, Tampa, FL, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Michael F Green
- Center on Enhancement of Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA, USA
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2
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Liu J, Bellows B, Hu XJ, Wu J, Zhou Z, Soteros C, Wang L. A new time-varying coefficient regression approach for analyzing infectious disease data. Sci Rep 2023; 13:14687. [PMID: 37673956 PMCID: PMC10482960 DOI: 10.1038/s41598-023-41551-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023] Open
Abstract
Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.
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Affiliation(s)
- Juxin Liu
- Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, S7N 5E6, Canada.
| | - Brandon Bellows
- Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, S7N 5E6, Canada
| | - X Joan Hu
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, V5A 1S6, Canada
| | - Jianhong Wu
- Department of Mathematics and Statistics, York University, Toronto, M3J 1P3, Canada
| | - Zhou Zhou
- Department of Statistical Sciences, University of Toronto, Toronto, M5G 1X6, Canada
| | - Chris Soteros
- Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, S7N 5E6, Canada
| | - Lin Wang
- Department of Mathematics and Statistics, University of New Brunswick, Fredericton, E3B 5A3, Canada
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3
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Das P, Peterson CB, Ni Y, Reuben A, Zhang J, Zhang J, Do KA, Baladandayuthapani V. Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer. Biometrics 2023; 79:2474-2488. [PMID: 36239535 PMCID: PMC10102253 DOI: 10.1111/biom.13774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 08/18/2022] [Indexed: 12/14/2022]
Abstract
The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell receptor (TCR) repertoire sequencing is a relatively new technology that enables monitoring of T-cells, a subset of immune cells that play a central role in modulating immune response. These immunologic relationships are complex and are governed by various distributional aspects of an individual patient's tumor profile. We propose Bayesian QUANTIle regression for hierarchical COvariates (QUANTICO) that allows simultaneous modeling of hierarchical relationships between multilevel covariates, conducts explicit variable selection, estimates quantile and patient-specific coefficient effects, to induce individualized inference. We show QUANTICO outperforms existing approaches in multiple simulation scenarios. We demonstrate the utility of QUANTICO to investigate the effect of TCR variables on immune response in a cohort of lung cancer patients. At population level, our analyses reveal the mechanistic role of T-cell proportion on the immune cell abundance, with tumor mutation burden as an important factor modulating this relationship. At a patient level, we find several outlier patients based on their quantile-specific coefficient functions, who have higher mutational rates and different smoking history.
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Affiliation(s)
- Priyam Das
- Dept. of Biomedical Informatics, Harvard Medical School
| | | | - Yang Ni
- Dept. of Statistics, Texas A&M University
| | - Alexandre Reuben
- Dept. of Thoracic Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center
| | - Jiexin Zhang
- Dept. of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center
| | - Jianjun Zhang
- Dept. of Thoracic Head and Neck Medical Oncology, University of Texas MD Anderson Cancer Center
| | - Kim-Anh Do
- Dept. of Biostatistics, University of Texas MD Anderson Cancer Center
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Sun Y, Kang J, Brummett C, Li Y. Individualized risk assessment of preoperative opioid use by interpretable neural network regression. Ann Appl Stat 2023; 17:434-453. [PMID: 37006707 PMCID: PMC10065608 DOI: 10.1214/22-aoas1634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Understanding the risk of preoperative opioid use helps establish patient-centered pain management. In the field of machine learning, deep neural network (DNN) has emerged as a powerful means for risk assessment because of its superb prediction power; however, the blackbox algorithms may make the results less interpretable than statistical models. Bridging the gap between the statistical and machine learning fields, we propose a novel Interpretable Neural Network Regression (INNER), which combines the strengths of statistical and DNN models. We use the proposed INNER to conduct individualized risk assessment of preoperative opioid use. Intensive simulations and an analysis of 34,186 patients expecting surgery in the Analgesic Outcomes Study (AOS) show that the proposed INNER not only can accurately predict the preoperative opioid use using preoperative characteristics as DNN, but also can estimate the patient-specific odds of opioid use without pain and the odds ratio of opioid use for a unit increase in the reported overall body pain, leading to more straight-forward interpretations of the tendency to use opioids than DNN. Our results identify the patient characteristics that are strongly associated with opioid use and is largely consistent with the previous findings, providing evidence that INNER is a useful tool for individualized risk assessment of preoperative opioid use.
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Affiliation(s)
- Yuming Sun
- Department of Biostatistics, University of Michigan
| | - Jian Kang
- Department of Biostatistics, University of Michigan
| | - Chad Brummett
- Department of Anesthesiology, University of Michigan
| | - Yi Li
- Department of Biostatistics, University of Michigan
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5
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Zhou Y, Hooker G. Decision tree boosted varying coefficient models. Data Min Knowl Discov 2022. [DOI: 10.1007/s10618-022-00863-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractVarying coefficient models are a flexible extension of generic parametric models whose coefficients are functions of a set of effect-modifying covariates instead of fitted constants. They are capable of achieving higher model complexity while preserving the structure of the underlying parametric models, hence generating interpretable predictions. In this paper we study the use of gradient boosted decision trees as those coefficient-deciding functions in varying coefficient models with linearly structured outputs. In contrast to the traditional choices of splines or kernel smoothers, boosted trees are more flexible since they require no structural assumptions in the effect modifier space. We introduce our proposed method from the perspective of a localized version of gradient descent, prove its theoretical consistency under mild assumptions commonly adapted by decision tree research, and empirically demonstrate that the proposed tree boosted varying coefficient models achieve high performance qualified by their training speed, prediction accuracy and intelligibility as compared to several benchmark algorithms.
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6
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Heterogeneity in general multinomial choice models. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-022-00642-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractDifferent voters behave differently at the polls, different students make different university choices, or different countries choose different health care systems. Many research questions important to social scientists concern choice behavior, which involves dealing with nominal dependent variables. Drawing on the principle of maximum random utility, we propose applying a flexible and general heterogeneous multinomial logit model to study differences in choice behavior. The model systematically accounts for heterogeneity that classical models do not capture, indicates the strength of heterogeneity, and permits examining which explanatory variables cause heterogeneity. As the proposed approach allows incorporating theoretical expectations about heterogeneity into the analysis of nominal dependent variables, it can be applied to a wide range of research problems. Our empirical example uses individual-level survey data to demonstrate the benefits of the model in studying heterogeneity in electoral decisions.
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7
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Penalized kernel quantile regression for varying coefficient models. J Stat Plan Inference 2022. [DOI: 10.1016/j.jspi.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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8
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Liang J, Tian M. Sparse regression for low-dimensional time-dynamic varying coefficient models with application to air quality data. J Appl Stat 2022; 50:1378-1399. [PMID: 37025280 PMCID: PMC10071989 DOI: 10.1080/02664763.2022.2028131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Time dynamic varying coefficient models play an important role in applications of biology, medicine, environment, finance, etc. Traditional methods, such as kernel smoothing and spline smoothing, are popular. But explicit expressions are unavailable using these methods, and the convergence rate of coefficient function estimators is slow. To address these problems, we expand the varying component with appropriate basis functions. And then we solve a sparse regression problem via a sequential thresholded least-squares estimator. The "parameterization" leads to explicit expressions and fast computation speed. Convergence of the sequential thresholded least squares algorithm is guaranteed. The asymptotic distribution of the coefficient function estimator is derived under certain assumptions. Our simulation shows the proposed method has higher precision and computing speed. Finally, our proposed method is applied to the study of PM 2.5 concentration in Beijing. We analyze the relationship between PM 2.5 and other impact factors.
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Affiliation(s)
- Jinwen Liang
- School of Statistics, Renmin University of China, Beijing, People's Republic of China
| | - Maozai Tian
- School of Statistics, Renmin University of China, Beijing, People's Republic of China
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
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9
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Chen J, Li D, Wei L, Zhang W. Nonparametric homogeneity pursuit in functional-coefficient models. J Nonparametr Stat 2021. [DOI: 10.1080/10485252.2021.1951265] [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)
- Jia Chen
- Department of Economics and Related Studies, University of York, York, UK
| | - Degui Li
- Department of Mathematics, University of York, York, UK
| | - Lingling Wei
- Department of Mathematics, University of York, York, UK
| | - Wenyang Zhang
- Department of Mathematics, University of York, York, UK
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10
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Park S, Lee ER. Hypothesis testing of varying coefficients for regional quantiles. Comput Stat Data Anal 2021. [DOI: 10.1016/j.csda.2021.107204] [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]
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11
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Zhao M, Xu X, Zhu Y, Zhang K, Zhou Y. Model estimation and selection for partial linear varying coefficient EV models with longitudinal data. J Appl Stat 2021; 50:512-534. [PMID: 36819082 PMCID: PMC9930868 DOI: 10.1080/02664763.2021.1904847] [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: 10/21/2022]
Abstract
In this paper, we consider the estimation and model selection for longitudinal partial linear varying coefficient errors-in-variables (EV) models when the covariates are measured with some additive errors. Bias-corrected penalized quadratic inference functions method is proposed based on quadratic inference functions with two penalty function terms. The proposed method can not only handle the measurement errors of covariates and within-subject correlations but also estimate and select significant non-zero parametric and nonparametric components simultaneously. With some regularization conditions, the resulting estimators of parameters are asymptotically normal and the estimators of nonparametric varying coefficient achieves the optimal convergence rate. Furthermore, we present simulation studies and a real example analysis to evaluate the finite sample performance of the proposed method.
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Affiliation(s)
- Mingtao Zhao
- School of Statistics and Applied Mathematics Anhui University of Finance & Economics, Bengbu, People's Republic of China
| | - Xiaoli Xu
- School of Management Science and Engineering, Anhui University of Finance & Economics, Bengbu, People's Republic of China
| | - Yanling Zhu
- School of Statistics and Applied Mathematics Anhui University of Finance & Economics, Bengbu, People's Republic of China
| | - Kongsheng Zhang
- School of Statistics and Applied Mathematics Anhui University of Finance & Economics, Bengbu, People's Republic of China
| | - Yan Zhou
- College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, People's Republic of China,Yan Zhou
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12
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Abstract
Summary
We introduce bootstrap tests for semiparametric generalized structured models. These can be used for testing different kinds of model specifications like separability, functional forms and homogeneity of effects, or for performing variable selection in a large class of semiparametric models. The test statistics are based on the comparison of non- and semiparametric alternatives in which both the null hypothesis and the alternative are non- or semiparametric. All estimators are obtained by smooth backfitting. Simulation studies show excellent performance of the test procedures.
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13
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Zhao M, Gao Y, Cui Y. Variable selection for longitudinal varying coefficient errors-in-variables models. COMMUN STAT-THEOR M 2020. [DOI: 10.1080/03610926.2020.1801738] [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)
- Mingtao Zhao
- School of Statistics and Applied Mathematics, Anhui University of Finance & Economics, Bengbu, Anhui, China
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
| | - Yuzhao Gao
- School of Public Health, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, USA
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14
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Affiliation(s)
- Myungjin Kim
- Department of Statistics, Iowa State University, Ames, IA
| | - Li Wang
- Department of Statistics, Iowa State University, Ames, IA
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15
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Chatla SB, Shmueli G. A Tree-Based Semi-Varying Coefficient Model for the COM-Poisson Distribution. J Comput Graph Stat 2020. [DOI: 10.1080/10618600.2020.1753530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Suneel Babu Chatla
- Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX
| | - Galit Shmueli
- Institute of Service Science, National Tsing Hua University, Hsinchu, Taiwan
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16
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Han K, Lee YK, Park BU. Smooth backfitting for errors-in-variables varying coefficient regression models. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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17
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Zhang X, Zhong Q, Wang JL. A new approach to varying-coefficient additive models with longitudinal covariates. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2020.106912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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18
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Data-driven model checking for errors-in-variables varying-coefficient models with replicate measurements. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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19
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Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model. ADV DATA ANAL CLASSI 2019. [DOI: 10.1007/s11634-019-00381-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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20
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Estimation of a rank-reduced functional-coefficient panel data model with serial correlation. J MULTIVARIATE ANAL 2019. [DOI: 10.1016/j.jmva.2019.04.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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21
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Affiliation(s)
- Takuma Yoshida
- Graduate School of Science and Engineering, Kagoshima University, Kagoshima, Japan
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22
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Chen Y, Zheng M, Lv J, Shi T, Liu P, Wu Y, Feng W, He W, Guo P. Interactions between ambient air pollutants and temperature on emergency department visits: Analysis of varying-coefficient model in Guangzhou, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 668:825-834. [PMID: 30870751 DOI: 10.1016/j.scitotenv.2019.03.049] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Revised: 03/04/2019] [Accepted: 03/04/2019] [Indexed: 02/05/2023]
Abstract
BACKGROUND At present, there are few studies on the effect of short-term interactions between ambient air pollutants and temperature on cause-specific emergency department visits in China. This study aimed to explore their short-term interactions on cause-specific emergency department visits using data collected from a total of 65 public hospitals in Guangzhou city, south China. MATERIAL AND METHODS We included a total of 226,443 emergency department visits which were diagnosed as neurological, respiratory and circulatory disease in Guangzhou from January 1, 2014 to December 31, 2017. Average daily concentrations of air pollutants including carbon monoxide (CO), particulate matter having a median diameter of 2.5 μm or less (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3) were collected from the Guangzhou Environmental Protection Bureau. We employed quasi-Poisson varying coefficient regression models to assess the interaction effects between air pollutants and daily temperature levels (DTLs) on emergency department visits for neurological, respiratory and circulatory diseases, respectively. RESULTS Average number of emergency department visits for neurological, respiratory and circulatory diseases were 92, 26 and 38, respectively. After controlling for other pollutants, meteorological factors and other time-varying confounders, we found the interactions between NO2 and the 1st DTL (3.4-17.1 °C), NO2 and the 2nd DTL (17.1-23.5 °C) for neurological emergency department visits were statistically significant, displaying a nonlinear relationship. Additionally, we found that the interactions between SO2 and the 4th DTL (27.4-31.1 °C) also had a significantly adverse effect on respiratory emergency department visits. CONCLUSIONS Our findings provide novel evidence on SO2-by-temperature interactions, and NO2-by-temperature interactions for emergency department visits of cause-specific diseases.
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Affiliation(s)
- Yuliang Chen
- Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, China
| | - Murui Zheng
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
| | - Jiayun Lv
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
| | - Tongxing Shi
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
| | - Pengda Liu
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
| | - Yan Wu
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China
| | - Wenru Feng
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China.
| | - Weiyun He
- Guangzhou Center for Disease Control and Prevention, Guangzhou 510440, China.
| | - Pi Guo
- Department of Preventive Medicine, Shantou University Medical College, Shantou 515041, China.
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23
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Wang Z, Xue L. Variance estimation for sparse ultra-high dimensional varying coefficient models. COMMUN STAT-THEOR M 2019. [DOI: 10.1080/03610926.2018.1429627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Zhaoliang Wang
- College of Applied Sciences, Beijing University of Technology, Beijing, China
- School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, China
| | - Liugen Xue
- College of Applied Sciences, Beijing University of Technology, Beijing, China
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24
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Liu Z, Liu C, Sun Z. Consistent model check of errors-in-variables varying-coefficient model with auxiliary variable. J Stat Plan Inference 2019. [DOI: 10.1016/j.jspi.2018.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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25
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Abstract
In this paper, we investigate several variable selection procedures to give an overview of the existing literature for practitioners. “Let the data speak for themselves” has become the motto of many applied researchers since the number of data has significantly grown. Automatic model selection has been promoted to search for data-driven theories for quite a long time now. However, while great extensions have been made on the theoretical side, basic procedures are still used in most empirical work, e.g., stepwise regression. Here, we provide a review of main methods and state-of-the art extensions as well as a topology of them over a wide range of model structures (linear, grouped, additive, partially linear and non-parametric) and available software resources for implemented methods so that practitioners can easily access them. We provide explanations for which methods to use for different model purposes and their key differences. We also review two methods for improving variable selection in the general sense.
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26
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Liu F, Li C, Wang P, Kang X. Empirical likelihood based diagnostics for heteroscedasticity in semiparametric varying-coefficient partially linear errors-in-variables models. COMMUN STAT-THEOR M 2018. [DOI: 10.1080/03610926.2017.1395050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Feng Liu
- The School of Science, Chongqing University of Technology, Chongqing, China
| | - Chunyan Li
- The School of Science, Chongqing University of Technology, Chongqing, China
| | - Pengfei Wang
- The School of Science, Chongqing University of Technology, Chongqing, China
| | - Xinmei Kang
- The School of Science, Chongqing University of Technology, Chongqing, China
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27
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Lee K, Lee YK, Park BU, Yang SJ. Time-dynamic varying coefficient models for longitudinal data. Comput Stat Data Anal 2018. [DOI: 10.1016/j.csda.2018.01.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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28
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Effects of Bank Lending on Urban Housing Prices for Sustainable Development: A Panel Analysis of Chinese Cities. SUSTAINABILITY 2018. [DOI: 10.3390/su10030642] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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29
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Schick A, Zhu Y, Du X. Estimation of the error distribution in a varying coefficient regression model. J Nonparametr Stat 2018. [DOI: 10.1080/10485252.2018.1429608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Anton Schick
- Department of Mathematical Sciences, Binghamton University, Binghamton, NY, USA
| | - Yilin Zhu
- Department of Mathematical Sciences, Binghamton University, Binghamton, NY, USA
| | - Xiaojie Du
- Department of Mathematical Sciences, Binghamton University, Binghamton, NY, USA
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Shim J, Sohn I, Hwang C. Kernel-based random effect time-varying coefficient model for longitudinal data. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.06.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Ma X, Chen X, Zhang J. Fast robust feature screening for ultrahigh-dimensional varying coefficient models. J STAT COMPUT SIM 2017. [DOI: 10.1080/00949655.2016.1223668] [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]
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Wan ATK, Xie S, Zhou Y. A varying coefficient approach to estimating hedonic housing price functions and their quantiles. J Appl Stat 2016. [DOI: 10.1080/02664763.2016.1238053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Alan T. K. Wan
- Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong
| | - Shangyu Xie
- School of Banking and Finance, University of International Business and Economics, Beijing, People's Republic of China
| | - Yong Zhou
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, People's Republic of China
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China
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Heller GZ, Manuguerra M, Chow R. How to analyze the Visual Analogue Scale: Myths, truths and clinical relevance. Scand J Pain 2016; 13:67-75. [PMID: 28850536 DOI: 10.1016/j.sjpain.2016.06.012] [Citation(s) in RCA: 276] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 06/07/2016] [Accepted: 06/30/2016] [Indexed: 01/17/2023]
Abstract
BACKGROUND AND AIMS The Visual Analogue Scale (VAS) is a popular tool for the measurement of pain. A variety of statistical methods are employed for its analysis as an outcome measure, not all of them optimal or appropriate. An issue which has attracted much discussion in the literature is whether VAS is at a ratio or ordinal level of measurement. This decision has an influence on the appropriate method of analysis. The aim of this article is to provide an overview of current practice in the analysis of VAS scores, to propose a method of analysis which avoids the shortcomings of more traditional approaches, and to provide best practice recommendations for the analysis of VAS scores. METHODS We report on the current usage of statistical methods, which fall broadly into two categories: those that assume a probability distribution for VAS, and those that do not. We give an overview of these methods, and propose continuous ordinal regression, an extension of current ordinal regression methodology, which is appropriate for VAS at an ordinal level of measurement. We demonstrate the analysis of a published data set using a variety of methods, and use simulation to compare the power of the various methods to detect treatment differences, in differing pain situations. RESULTS We demonstrate that continuous ordinal regression provides the most powerful statistical analysis under a variety of conditions. CONCLUSIONS AND IMPLICATIONS We recommend that in the situation in which no covariates besides treatment group are included in the analysis, distribution-free methods (Wilcoxon, Mann-Whitney) be used, as their power is indistinguishable from that of the proposed method. In the situation in which there are covariates which affect VAS, the proposed method is optimal. However, in this case, if the VAS scores are not concentrated around either extreme of the scale, normal-distribution methods (t-test, linear regression) are almost as powerful, and are recommended as a pragmatic choice. In the case of small sample size and VAS skewed to either extreme of the scale, the proposed method has vastly superior power to other methods.
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Affiliation(s)
- Gillian Z Heller
- Department of Statistics, Faculty of Science and Engineering, Macquarie University, NSW, 2109, Australia
| | - Maurizio Manuguerra
- Department of Statistics, Faculty of Science and Engineering, Macquarie University, NSW, 2109, Australia
| | - Roberta Chow
- Brain and Mind Centre, 94 Mallett Street, Camperdown, NSW 2050, Australia
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Lee ER, Mammen E. Local linear smoothing for sparse high dimensional varying coefficient models. Electron J Stat 2016. [DOI: 10.1214/16-ejs1110] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Wu C, Shi X, Cui Y, Ma S. A penalized robust semiparametric approach for gene-environment interactions. Stat Med 2015; 34:4016-30. [PMID: 26239060 DOI: 10.1002/sim.6609] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Revised: 06/28/2015] [Accepted: 07/06/2015] [Indexed: 11/09/2022]
Abstract
In genetic and genomic studies, gene-environment (G×E) interactions have important implications. Some of the existing G×E interaction methods are limited by analyzing a small number of G factors at a time, by assuming linear effects of E factors, by assuming no data contamination, and by adopting ineffective selection techniques. In this study, we propose a new approach for identifying important G×E interactions. It jointly models the effects of all E and G factors and their interactions. A partially linear varying coefficient model is adopted to accommodate possible nonlinear effects of E factors. A rank-based loss function is used to accommodate possible data contamination. Penalization, which has been extensively used with high-dimensional data, is adopted for selection. The proposed penalized estimation approach can automatically determine if a G factor has an interaction with an E factor, main effect but not interaction, or no effect at all. The proposed approach can be effectively realized using a coordinate descent algorithm. Simulation shows that it has satisfactory performance and outperforms several competing alternatives. The proposed approach is used to analyze a lung cancer study with gene expression measurements and clinical variables. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Cen Wu
- Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT, 06520, U.S.A.,Department of Statistics, Kansas State University, 1116 Mid-Campus Drive N., Manhattan, KS, 66506, U.S.A
| | - Xingjie Shi
- Department of Statistics, Nanjing University of Finance and Economics, Nanjing, China
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, 619 Red Cedar Rd, East Lansing, MI, 48824, U.S.A
| | - Shuangge Ma
- Department of Biostatistics, School of Public Health, Yale University, 60 College Street, New Haven, CT, 06520, U.S.A.,VA Cooperative Studies Program Coordinating Center, West Haven, CT, 06516, U.S.A
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Sun L, Wu R. Toward the practical utility of systems mapping: Reply to comments on "Mapping complex traits as a dynamic system". Phys Life Rev 2015; 13:198-201. [PMID: 26009264 DOI: 10.1016/j.plrev.2015.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 04/29/2015] [Indexed: 11/19/2022]
Affiliation(s)
- Lidan Sun
- Beijing Key Laboratory of Ornamental Plants Germplasm Innovation and Molecular Breeding, National Engineering Research Center for Floriculture, College of Landscape Architecture, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA
| | - Rongling Wu
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
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Sperlich S, Theler R. Modeling heterogeneity: a praise for varying-coefficient models in causal analysis. Comput Stat 2015. [DOI: 10.1007/s00180-015-0581-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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