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

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

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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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