scholarly journals Assessment of Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17295 ◽  
Author(s):  
Jenna Czarnota ◽  
Chris Gennings ◽  
David C. Wheeler
Keyword(s):  
Cancer Risk ◽  
Strong Association ◽  
Body Burden ◽  
Penalized Regression ◽  
Environmental Chemicals ◽  
Adaptive Lasso ◽  
Environmental Chemical ◽  
Site Specific ◽  
Regression Methods ◽  
Correlated Components

In evaluation of cancer risk related to environmental chemical exposures, the effect of many chemicals on disease is ultimately of interest. However, because of potentially strong correlations among chemicals that occur together, traditional regression methods suffer from collinearity effects, including regression coefficient sign reversal and variance inflation. In addition, penalized regression methods designed to remediate collinearity may have limitations in selecting the truly bad actors among many correlated components. The recently proposed method of weighted quantile sum (WQS) regression attempts to overcome these problems by estimating a body burden index, which identifies important chemicals in a mixture of correlated environmental chemicals. Our focus was on assessing through simulation studies the accuracy of WQS regression in detecting subsets of chemicals associated with health outcomes (binary and continuous) in site-specific analyses and in non-site-specific analyses. We also evaluated the performance of the penalized regression methods of lasso, adaptive lasso, and elastic net in correctly classifying chemicals as bad actors or unrelated to the outcome. We based the simulation study on data from the National Cancer Institute Surveillance Epidemiology and End Results Program (NCI-SEER) case-control study of non-Hodgkin lymphoma (NHL) to achieve realistic exposure situations. Our results showed that WQS regression had good sensitivity and specificity across a variety of conditions considered in this study. The shrinkage methods had a tendency to incorrectly identify a large number of components, especially in the case of strong association with the outcome.

scholarly journals The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO

2018 ◽  
Vol 61 (4) ◽  
pp. 451-458
Author(s):  
Suna Akkol
Keyword(s):  
Live Weight ◽  
Information Criterion ◽  
Penalized Regression ◽  
Adaptive Lasso ◽  
Model Complexity ◽  
Coefficient Of Determination ◽  
Explanatory Variables ◽  
Regression Methods ◽  
Bayesian Criterion ◽  
Chest Girth

Abstract. The least absolute selection and shrinkage operator (LASSO) and adaptive LASSO methods have become a popular model in the last decade, especially for data with a multicollinearity problem. This study was conducted to estimate the live weight (LW) of Hair goats from biometric measurements and to select variables in order to reduce the model complexity by using penalized regression methods: LASSO and adaptive LASSO for γ=0.5 and γ=1. The data were obtained from 132 adult goats in Honaz district of Denizli province. Age, gender, forehead width, ear length, head length, chest width, rump height, withers height, back height, chest depth, chest girth, and body length were used as explanatory variables. The adjusted coefficient of determination (Radj2), root mean square error (RMSE), Akaike's information criterion (AIC), Schwarz Bayesian criterion (SBC), and average square error (ASE) were used in order to compare the effectiveness of the methods. It was concluded that adaptive LASSO (γ=1) estimated the LW with the highest accuracy for both male (Radj2=0.9048; RMSE = 3.6250; AIC = 79.2974; SBC = 65.2633; ASE = 7.8843) and female (Radj2=0.7668; RMSE = 4.4069; AIC = 392.5405; SBC = 308.9888; ASE = 18.2193) Hair goats when all the criteria were considered.

scholarly journals High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking

2019 ◽  
Vol 30 (3) ◽  
pp. 697-719 ◽  
Author(s):  
Fan Wang ◽  
Sach Mukherjee ◽  
Sylvia Richardson ◽  
Steven M. Hill
Keyword(s):  
Variable Selection ◽  
Large Scale ◽  
Penalized Regression ◽  
Adaptive Lasso ◽  
High Dimensional ◽  
Finite Sample ◽  
Dantzig Selector ◽  
Regression Methods ◽  
High Dimensional Regression ◽  
Selection And Ranking

AbstractPenalized likelihood approaches are widely used for high-dimensional regression. Although many methods have been proposed and the associated theory is now well developed, the relative efficacy of different approaches in finite-sample settings, as encountered in practice, remains incompletely understood. There is therefore a need for empirical investigations in this area that can offer practical insight and guidance to users. In this paper, we present a large-scale comparison of penalized regression methods. We distinguish between three related goals: prediction, variable selection and variable ranking. Our results span more than 2300 data-generating scenarios, including both synthetic and semisynthetic data (real covariates and simulated responses), allowing us to systematically consider the influence of various factors (sample size, dimensionality, sparsity, signal strength and multicollinearity). We consider several widely used approaches (Lasso, Adaptive Lasso, Elastic Net, Ridge Regression, SCAD, the Dantzig Selector and Stability Selection). We find considerable variation in performance between methods. Our results support a “no panacea” view, with no unambiguous winner across all scenarios or goals, even in this restricted setting where all data align well with the assumptions underlying the methods. The study allows us to make some recommendations as to which approaches may be most (or least) suitable given the goal and some data characteristics. Our empirical results complement existing theory and provide a resource to compare methods across a range of scenarios and metrics.

Multiple structural breaks in cointegrating regressions: a model selection approach

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexander Schmidt ◽  
Karsten Schweikert
Keyword(s):  
Structural Breaks ◽  
Penalized Regression ◽  
Adaptive Lasso ◽  
Test Statistics ◽  
New Approach ◽  
Multiple Structural Breaks ◽  
Long Run ◽  
Model Selection Approach ◽  
Regression Techniques ◽  
Lasso Estimator

Abstract In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with known breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.

Penalized Regression Methods in the Source Analysis of Face Recognition

2008 ◽  
pp. 621-624
Author(s):  
Mayrim Vega-Hernández ◽  
Eduardo Martínez-Montes ◽  
Jhoanna Pérez-Hidalgo-Gato ◽  
José M. Sánchez-Bornot ◽  
Pedro Valdés-Sosa
Keyword(s):  
Face Recognition ◽  
Penalized Regression ◽  
Source Analysis ◽  
Regression Methods

scholarly journals Diabetes Mellitus and Site-specific Colorectal Cancer Risk in Korea: A Case-control Study

2015 ◽  
Vol 49 (1) ◽  
pp. 45-52 ◽  
Author(s):  
Hyeongtaek Woo ◽  
Jeeyoo Lee ◽  
Jeonghee Lee ◽  
Ji Won Park ◽  
Sungchan Park ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Colorectal Cancer ◽  
Cancer Risk ◽  
Case Control Study ◽  
Case Control ◽  
Colorectal Cancer Risk ◽  
Site Specific ◽  
Control Study

CHAPTER 12. Environmental Chemicals and Prostate Cancer Risk

2012 ◽  
pp. 331-345
Author(s):  
Jaspreet S. Joneja ◽  
Wen-Yang Hu ◽  
Ricardo R. Rios ◽  
Gail S. Prins'
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Prostate Cancer Risk ◽  
Environmental Chemicals

A scalable workflow for the human exposome

2020 ◽  
Author(s):  
Xin Hu ◽  
Douglas Walker ◽  
YongLiang Liang ◽  
Matthew Smith ◽  
Michael Orr ◽  
...  
Keyword(s):  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
A Priori ◽  
Population Level ◽  
Sample Volume ◽  
Single Step ◽  
Environmental Chemicals ◽  
Environmental Chemical ◽  
Mass Spectral ◽  
Sensitivity And Selectivity

Abstract Complementing the genome with an understanding of the human exposome is an important challenge for contemporary science and technology. Tens of thousands of chemicals are used in commerce, yet cost for targeted environmental chemical analysis limits surveillance to a few hundred known hazards. To overcome limitations which prevent scaling to thousands of chemicals, we developed a single-step express liquid extraction (XLE), gas chromatography high-resolution mass spectrometry (GC-HRMS) analysis and computational pipeline to operationalize the human exposome. We show that the workflow supports quantification of environmental chemicals in small human plasma (200 µL) and tissue (≤ 100 mg) samples. The method also provides high resolution, sensitivity and selectivity for exposome epidemiology of mass spectral features without a priori knowledge of chemical identity. The simplicity of the method can facilitate harmonization of environmental biomonitoring between laboratories and enable population level human exposome research with limited sample volume.

scholarly journals Albuminuria, Kidney Function, and Cancer Risk in the Community

2020 ◽  
Vol 189 (9) ◽  
pp. 942-950
Author(s):  
Yejin Mok ◽  
Shoshana H Ballew ◽  
Yingying Sang ◽  
Josef Coresh ◽  
Corinne E Joshu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Kidney Disease ◽  
Cancer Risk ◽  
Cancer Incidence ◽  
Prostate Cancer Screening ◽  
Sensitivity Analyses ◽  
Site Specific ◽  
Specific Cancer ◽  
Shared Risk

Abstract Few studies have comprehensively investigated the association of 2 key kidney disease measures, estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR), with cancer incidence. In 8,935 participants at the baseline (1996–1998) from the Atherosclerosis Risk in Communities study, we quantified the associations of eGFR (based on creatinine and cystatin C) and ACR with cancer risk using Cox regression models adjusted for potential confounders. Due to changing guidelines for prostate cancer screening during the follow-up period, we investigated overall cancer, overall nonprostate cancer, and site-specific cancer. During a median follow-up of 14.7 years, 2,030 incident cancer cases occurred. In demographically adjusted models, low eGFR and high ACR were associated with cancer incidence (both overall and overall nonprostate cancer). These associations were attenuated after adjusting for other shared risk factors, with a significant association remaining only for ACR (≥103 compared with 5 mg/g) and overall nonprostate cancer. For site-specific cancer, only high ACR showed a significant association with lung and urinary tract cancers. Of these, the association between ACR and lung cancer appeared most robust in several sensitivity analyses. Kidney disease measures, particularly high ACR, were independently associated with cancer risk. The association between ACR and lung cancer was uniquely robust, warranting future studies to explore potential mechanisms.

Exposure to environmental chemicals and type 1 diabetes: an update

2019 ◽  
Vol 73 (6) ◽  
pp. 483-488 ◽  
Author(s):  
Sarah G Howard
Keyword(s):  
Type 1 Diabetes ◽  
Experimental Studies ◽  
Epidemiological Studies ◽  
Environmental Chemicals ◽  
Exposure Level ◽  
Environmental Chemical ◽  
Timing Of Exposure

This narrative review summarises recently published epidemiological and in vivo experimental studies on exposure to environmental chemicals and their potential role in the development of type 1 diabetes mellitus (T1DM). These studies focus on a variety of environmental chemical exposures, including to air pollution, arsenic, some persistent organic pollutants, pesticides, bisphenol A and phthalates. Of the 15 epidemiological studies identified, 14 include measurements of exposures during childhood, 2 include prenatal exposures and 1 includes adults over age 21. Together, they illustrate that the role of chemicals in T1DM may be complex and may depend on a variety of factors, such as exposure level, timing of exposure, nutritional status and chemical metabolism. While the evidence that these exposures may increase the risk of T1DM is still preliminary, it is critical to investigate this possibility further as a means of preventing T1DM.

scholarly journals Optimized application of penalized regression methods to diverse genomic data

2011 ◽  
Vol 27 (24) ◽  
pp. 3399-3406 ◽  
Author(s):  
Levi Waldron ◽  
Melania Pintilie ◽  
Ming-Sound Tsao ◽  
Frances A. Shepherd ◽  
Curtis Huttenhower ◽  
...  
Keyword(s):  
Genomic Data ◽  
Penalized Regression ◽  
Regression Methods
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