Examining the Relationship between Multi-Pollutant Profiles and Measures of Deprivation using Bayesian Dirichlet-Process Mixture Models

Abstract

Standard regression analyses are often plagued with problems encountered when one tries to make inference going beyond main effects, using datasets that contain dozens of variables that are potentially correlated. This situation arises, for example, in exposure assessment studies where a number of highly correlated measures of air pollution exposure are to be associated with health-related outcomes. We propose a method that addresses these problems by using, as its basic unit of inference, a profile formed from a sequence of covariate values. These covariate profiles are clustered into groups and associated via a regression model to a relevant outcome. The Bayesian clustering aspect of the proposed modeling framework has a number of advantages over traditional clustering approaches in that it allows the number of groups to vary, uncovers subgroups and examines their association with an outcome of interest and fits the model as a unit, allowing an individual's outcome potentially to influence cluster membership. The method is demonstrated with an analysis of the association between multi-pollutant profiles and measures of deprivation corresponding to census tracts in Los Angeles County. Standard regression analyses are often plagued with problems encountered when one tries to make inference going beyond main effects, using datasets that contain dozens of variables that are potentially correlated. This situation arises, for example, in exposure assessment studies where a number of highly correlated measures of air pollution exposure are to be associated with health-related outcomes. We propose a method that addresses these problems by using, as its basic unit of inference, a profile formed from a sequence of covariate values. These covariate profiles are clustered into groups and associated via a regression model to a relevant outcome. The Bayesian clustering aspect of the proposed modeling framework has a number of advantages over traditional clustering approaches in that it allows the number of groups to vary, uncovers subgroups and examines their association with an outcome of interest and fits the model as a unit, allowing an individual's outcome potentially to influence cluster membership. The method is demonstrated with an analysis of the association between multi-pollutant profiles and measures of deprivation corresponding to census tracts in Los Angeles County.