Reduced hierarchical models with application to estimating health effects of simultaneous exposure to multiple pollutants.

ABSTRACT: Hierarchical models (HM) have been used extensively in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for other pollutants and other time-varying factors. Recently, Environmental Protection Agency (EPA) has called for research quantifying health effects of simultaneous exposure to many air pollutants. However, straightforward application of HM in this context is challenged by the need to specify a random-effect distribution on a high-dimensional vector of nuisance parameters. Here we introduce reduced HM as a general statistical approach for analyzing correlated data with many nuisance parameters. For reduced HM we first calculate the integrated likelihood of the parameter of interest (e.g. excess number of deaths

SUBMITTER: Bobb JF 

PROVIDER: S-EPMC3864808 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature