Time-varying coefficient models for the analysis of air pollution and health outcome data.
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.
Lee
Duncan
D
Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK. d.p.lee@maths.bath.ac.uk
Shaddick
Gavin
G
D000397:Air Pollution / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D006305:Health Status Indicators; D006801:Humans; D009014:Morals; D017711:Nonlinear Dynamics; D017063:Outcome Assessment, Health Care / Q000379:methods*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D016019:Survival Analysis; D015996:Survival Rate