Περίληψη:
A major statistical challenge in air pollution and health time-series
studies is to adequately control for confounding effects of time-varying
covariates. Daily health outcome counts are most commonly analysed by
Poisson regression models, adjusted for overdispersion, with air
pollution levels included as a linear predictor and smooth functions for
calendar time and weather variables to adjust for time-varying
confounders. Various smoothers have been used so far, but the optimal
strategy for choosing smoothers and their degree of smoothing remains
controversial. In this work, we evaluate the performance of various
smoothers with different criteria for choosing the degree of smoothing
in terms of bias and efficiency of the air pollution effect estimate in
a simulation study. The evaluated approaches were also applied to real
mortality data from 22 European cities. The simulation study imitated a
multi-city study. Data were generated from a fully parametric model.
Model selection methods which optimize prediction may lead to increased
biases in the air pollution effect estimate. Minimization of the
absolute value of the sum of the partial autocorrelation function of the
model’s residuals (PACF), as a criterion to choose the degree of
smoothness, gave the smallest biases. The penalized splines (PS) method
with a large number of effective dfs (e.g. 8-12 per year) could be used
as the basic, relatively conservative, analysis whereas the PS and
natural splines in combination with PACF could be applied to provide a
reasonable range of the effect estimate. Copyright (c) 2006 John Wiley
& Sons, Ltd.
Συγγραφείς:
Touloumi, G.
Samoli, E.
Pipikou, M.
Le Tertre, A. and
Atkinson, R.
Katsouyanni, K.
