A Bayesian mixture modeling approach for assessing the effects of correlated exposures in case-control studies

Frank de Vocht*, Nicola Cherry, Jon Wakefield

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)

Abstract

Predisposition to a disease is usually caused by cumulative effects of a multitude of exposures and lifestyle factors in combination with individual susceptibility. Failure to include all relevant variables may result in biased risk estimates and decreased power, whereas inclusion of all variables may lead to computational difficulties, especially when variables are correlated. We describe a Bayesian Mixture Model (BMM) incorporating a variable-selection prior and compared its performance with logistic multiple regression model (LM) in simulated case-control data with up to twenty exposures with varying prevalences and correlations. In addition, as a practical example we reanalyzed data on male infertility and occupational exposures (Chaps-UK). BMM mean-squared errors (MSE) were smaller than of the LM, and were independent of the number of model parameters. BMM type I errors were minimal (

Original languageEnglish
Pages (from-to)352-360
Number of pages9
JournalJournal of Exposure Science and Environmental Epidemiology
Volume22
Issue number4
DOIs
Publication statusPublished - 2012

Keywords

  • empirical/statistical models
  • exposure modeling
  • epidemiology
  • population based studies
  • volatile organic compounds
  • VARIABLE SELECTION
  • EPISTEMOLOGICAL MODESTY
  • CANCER-EPIDEMIOLOGY
  • MULTIPLE EXPOSURES
  • GENE-ENVIRONMENT
  • MALE-INFERTILITY
  • GLYCOL ETHERS
  • RISK-FACTORS
  • LUNG-CANCER
  • REGRESSION

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