Lee, D and Shaddick, G  ORCID: https://orcid.org/0000-0002-4117-4264
2007.
Time-varying coefficient models for the analysis of air pollution and health outcome data.
Biometrics
63
(4)
, pp. 1253-1261.
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.
| Item Type: | Article |
|---|---|
| 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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