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Time-varying coefficient models for the analysis of air pollution and health outcome data


Reference:

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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Official URL:

http://dx.doi.org/10.1111/j.1541-0420.2007.00776.x

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.

Details

Item Type Articles
CreatorsLee, D.and Shaddick, G.
DOI10.1111/j.1541-0420.2007.00776.x
DepartmentsFaculty of Science > Mathematical Sciences
RefereedYes
StatusPublished
ID Code6965
Additional InformationID number: ISI:000251508300031

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