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Investigating the time-varying effects of air pollution using distributed lagged models: A comparison of polynomial and window models
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Investigating the time-varying effects of air pollution using distributed lagged models: A comparison of polynomial and window models

A Barnett, Gail M Williams, Anne H Neller, Trudi Best and Rodney W Simpson
Epidemiology, Vol.15(5), p.S155
Conference of the International Society for Environmental Epidemiology (ISEE), 16th (New York, United States, 01-Aug-2004–04-Aug-2004)
2004
url
http://www.epidem.com/pt/re/epidemiology/fulltext.00001648-200407000-00398.htmView
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Abstract

Epidemiology air pollution distributed lag model
Polynomial Distributed Lag (PDL) models are a useful method for studying the long-term effects of air pollution exposure on morbidity and mortality. The model assumes that the past effects of air pollution (in days) vary smoothly according to a parametric polynomial shape. The model's key parameters are the order of the polynomial and the number of past days (P); both of which are ideally chosen to give an optimal fit to the data. In making this optimal selection two problems occur: 1) increasing the number of past days (P) does not add extra terms to the Akaike Information Criteria (AIC), and so it cannot be used to assess the optimal value of P; 2) the polynomial assumption means that very non-linear patterns require a high order model. In this paper, we tackled these problems by fitting a non-parametric window to a set of unconstrained lagged covariates, and used the Deviance Information Criteria (DIC) to select the optimal value of P.

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