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Examining the Joint Effect of Multiple Risk Factors Using Exposure Risk Profiles: Lung Cancer in Nonsmokers

Profile regression is a Bayesian statistical approach designed for investigating the joint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inference the exposure profiles of the subjects that is, the sequence of covariate values that correspond to each subject... Full description

Journal Title: Environmental Health Perspectives Oct 4, 2010, Vol.119(1), pp.84-91
Main Author: Papathomas, Michail
Other Authors: Molitor, John , Richardson, Sylvia , Riboli, Elio , Vineis, Paolo
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0091-6765 ; E-ISSN: 1552-9924 ; DOI: 10.1289/ehp.1002118
Link: http://search.proquest.com/docview/860393591/?pq-origsite=primo
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title: Examining the Joint Effect of Multiple Risk Factors Using Exposure Risk Profiles: Lung Cancer in Nonsmokers
format: Article
creator:
  • Papathomas, Michail
  • Molitor, John
  • Richardson, Sylvia
  • Riboli, Elio
  • Vineis, Paolo
subjects:
  • Statistics
  • Data Processing
  • Bayesian Analysis
  • Particulate Matter
  • Population Studies
  • Carcinogens
  • Nutrition
  • Nitrogen Dioxide
  • Pollutants
  • Classification
  • Risk Factors
  • Risk Groups
  • Lung Cancer
  • Air Pollution
  • Nitrogen Dioxide
  • Aerodynamics
  • Carcinogens
  • Particulates
  • Nutrition
  • Cancer
  • Lung Cancer
  • Air Pollution
  • Medical and Environmental Health
  • Air Pollution
  • Methods
  • Air Pollutants
  • Bayesian Inference
  • Clustering
  • Combined Effect
  • Gene/Environment Interactions
ispartof: Environmental Health Perspectives, Oct 4, 2010, Vol.119(1), pp.84-91
description: Profile regression is a Bayesian statistical approach designed for investigating the joint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inference the exposure profiles of the subjects that is, the sequence of covariate values that correspond to each subject. We applied profile regression to a case-control study of lung cancer in nonsmokers, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, to estimate the combined effect of environmental carcinogens and to explore possible gene-environment interactions. We tailored and extended the profile regression approach to the analysis of case-control studies, allowing for the analysis of ordinal data and the computation of posterior odds ratios. We compared and contrasted our results with those obtained using standard logistic regression and classification tree methods, including multifactor dimensionality reduction. Profile regression strengthened previous observations in other study populations on the role of air pollutants, particularly particulate matter less than or equal to 10 mu m in aerodynamic diameter (PM10), in lung cancer for nonsmokers. Covariates including living on a main road, exposure to PM10 and nitrogen dioxide, and carrying out manual work characterized high-risk subject profiles. Such combinations of risk factors were consistent with a priori expectations. In contrast, other methods gave less interpretable results. We conclude that profile regression is a powerful tool for identifying risk profiles that express the joint effect of etiologically relevant variables in multifactorial diseases.
language: eng
source:
identifier: ISSN: 0091-6765 ; E-ISSN: 1552-9924 ; DOI: 10.1289/ehp.1002118
fulltext: fulltext
issn:
  • 00916765
  • 0091-6765
  • 15529924
  • 1552-9924
url: Link


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titleExamining the Joint Effect of Multiple Risk Factors Using Exposure Risk Profiles: Lung Cancer in Nonsmokers
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subjectStatistics ; Data Processing ; Bayesian Analysis ; Particulate Matter ; Population Studies ; Carcinogens ; Nutrition ; Nitrogen Dioxide ; Pollutants ; Classification ; Risk Factors ; Risk Groups ; Lung Cancer ; Air Pollution ; Nitrogen Dioxide ; Aerodynamics ; Carcinogens ; Particulates ; Nutrition ; Cancer ; Lung Cancer ; Air Pollution ; Medical and Environmental Health ; Air Pollution ; Methods ; Air Pollutants ; Bayesian Inference ; Clustering ; Combined Effect ; Gene/Environment Interactions
descriptionProfile regression is a Bayesian statistical approach designed for investigating the joint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inference the exposure profiles of the subjects that is, the sequence of covariate values that correspond to each subject. We applied profile regression to a case-control study of lung cancer in nonsmokers, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, to estimate the combined effect of environmental carcinogens and to explore possible gene-environment interactions. We tailored and extended the profile regression approach to the analysis of case-control studies, allowing for the analysis of ordinal data and the computation of posterior odds ratios. We compared and contrasted our results with those obtained using standard logistic regression and classification tree methods, including multifactor dimensionality reduction. Profile regression strengthened previous observations in other study populations on the role of air pollutants, particularly particulate matter less than or equal to 10 mu m in aerodynamic diameter (PM10), in lung cancer for nonsmokers. Covariates including living on a main road, exposure to PM10 and nitrogen dioxide, and carrying out manual work characterized high-risk subject profiles. Such combinations of risk factors were consistent with a priori expectations. In contrast, other methods gave less interpretable results. We conclude that profile regression is a powerful tool for identifying risk profiles that express the joint effect of etiologically relevant variables in multifactorial diseases.
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titleExamining the Joint Effect of Multiple Risk Factors Using Exposure Risk Profiles: Lung Cancer in Nonsmokers
descriptionProfile regression is a Bayesian statistical approach designed for investigating the joint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inference the exposure profiles of the subjects that is, the sequence of covariate values that correspond to each subject. We applied profile regression to a case-control study of lung cancer in nonsmokers, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, to estimate the combined effect of environmental carcinogens and to explore possible gene-environment interactions. We tailored and extended the profile regression approach to the analysis of case-control studies, allowing for the analysis of ordinal data and the computation of posterior odds ratios. We compared and contrasted our results with those obtained using standard logistic regression and classification tree methods, including multifactor dimensionality reduction. Profile regression strengthened previous observations in other study populations on the role of air pollutants, particularly particulate matter less than or equal to 10 mu m in aerodynamic diameter (PM10), in lung cancer for nonsmokers. Covariates including living on a main road, exposure to PM10 and nitrogen dioxide, and carrying out manual work characterized high-risk subject profiles. Such combinations of risk factors were consistent with a priori expectations. In contrast, other methods gave less interpretable results. We conclude that profile regression is a powerful tool for identifying risk profiles that express the joint effect of etiologically relevant variables in multifactorial diseases.
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titleExamining the Joint Effect of Multiple Risk Factors Using Exposure Risk Profiles: Lung Cancer in Nonsmokers
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abstractProfile regression is a Bayesian statistical approach designed for investigating the joint effect of multiple risk factors. It reduces dimensionality by using as its main unit of inference the exposure profiles of the subjects that is, the sequence of covariate values that correspond to each subject. We applied profile regression to a case-control study of lung cancer in nonsmokers, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, to estimate the combined effect of environmental carcinogens and to explore possible gene-environment interactions. We tailored and extended the profile regression approach to the analysis of case-control studies, allowing for the analysis of ordinal data and the computation of posterior odds ratios. We compared and contrasted our results with those obtained using standard logistic regression and classification tree methods, including multifactor dimensionality reduction. Profile regression strengthened previous observations in other study populations on the role of air pollutants, particularly particulate matter less than or equal to 10 mu m in aerodynamic diameter (PM10), in lung cancer for nonsmokers. Covariates including living on a main road, exposure to PM10 and nitrogen dioxide, and carrying out manual work characterized high-risk subject profiles. Such combinations of risk factors were consistent with a priori expectations. In contrast, other methods gave less interpretable results. We conclude that profile regression is a powerful tool for identifying risk profiles that express the joint effect of etiologically relevant variables in multifactorial diseases.
doi10.1289/ehp.1002118
urlhttp://search.proquest.com/docview/860393591/
date2011-01-01