Bootstrap model averaging in time series studies of particulate matter air pollution and mortality
Date
2006
Authors
Martin, Michael
Roberts, Steven
Journal Title
Journal ISSN
Volume Title
Publisher
Nature Publishing Group
Abstract
The consensus from time series studies that have investigated the mortality effects of participate matter air pollution (PM) is that increases in PM are associated with increases in daily mortality. However, recently concerns have been raised that the observed positive association between PM and mortality may be an artefact of model selection due to multiple hypothesis testing. This problem arises when a number of models are investigated, but only the "best" model is reported and all subsequent inference is based on this model, ignoring the model selection process. In this paper, we introduce the use of the bootstrap as a means of addressing the problems of model selection in PM mortality time series studies. Using the bootstrap to perform inference about the effect of PM on mortality is a process based on a set of models rather than on a single model. It is shown that using the bootstrap to overcome the problems of model selection is competitive with the existing methodology of Bayesian model averaging.
Description
Keywords
Keywords: air pollution; article; averaging; Bayes theorem; methodology; model; mortality; particulate matter; Air Pollutants; Confounding Factors (Epidemiology); Humans; Models, Theoretical; Mortality; Particle Size Air pollution; Bayesian model averaging; Model selection; Mortality; PM; Time series
Citation
Collections
Source
Journal of Exposure Science and Environmental Epidemiology
Type
Journal article
Book Title
Entity type
Access Statement
License Rights
Restricted until
2037-12-31
Downloads
File
Description