Lee, D and Shaddick, G ![]() |
Abstract
In this article a time-varying coefficient model is developed to examine the relationship between adverse health and short-term (acute) exposure to air pollution. This model allows the relative risk to evolve over time, which may be due to an interaction with temperature, or from a change in the composition of pollutants, such as particulate matter, over time. The model produces a smooth estimate of these time-varying effects, which are not constrained to follow a fixed parametric form set by the investigator. Instead, the shape is estimated from the data using penalized natural cubic splines. Poisson regression models, using both quasi-likelihood and Bayesian techniques, are developed, with estimation performed using an iteratively re-weighted least squares procedure and Markov chain Monte Carlo simulation, respectively. The efficacy of the methods to estimate different types of time-varying effects are assessed via a simulation study, and the models are then applied to data from four cities that were part of the National Morbidity, Mortality, and Air Pollution Study.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | ?? VCO ?? |
Publisher: | Wiley |
ISSN: | 0006-341X |
Date of Acceptance: | 1 December 2006 |
Last Modified: | 02 Aug 2024 15:00 |
URI: | https://orca.cardiff.ac.uk/id/eprint/170753 |
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