Time-varying coefficient models for the analysis of air pollution and health outcome data
Lee, D. and Shaddick, G., 2007. Time-varying coefficient models for the analysis of air pollution and health outcome data. Biometrics, 63 (4), pp. 1253-1261.
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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.
|Creators||Lee, D.and Shaddick, G.|
|Departments||Faculty of Science > Mathematical Sciences|
|Additional Information||ID number: ISI:000251508300031|
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