TimeâVarying Coefficient Models for the Analysis of Air Pollution and Health Outcome Data
2007
Lee, Duncan | Shaddick, Gavin
In this article a timeâvarying coefficient model is developed to examine the relationship between adverse health and shortâterm (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 timeâvarying 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 quasiâlikelihood and Bayesian techniques, are developed, with estimation performed using an iteratively reâweighted least squares procedure and Markov chain Monte Carlo simulation, respectively. The efficacy of the methods to estimate different types of timeâvarying 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.
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