Journal Article

On the Use of Generalized Additive Models in Time-Series Studies of Air Pollution and Health

Francesca Dominici, Aidan McDermott, Scott L. Zeger and Jonathan M. Samet

in American Journal of Epidemiology

Published on behalf of Johns Hopkins Bloomberg School of Public Health

Volume 156, issue 3, pages 193-203
Published in print August 2002 | ISSN: 0002-9262
Published online August 2002 | e-ISSN: 1476-6256 | DOI:
On the Use of Generalized Additive Models in Time-Series Studies of Air Pollution and Health

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The widely used generalized additive models (GAM) method is a flexible and effective technique for conducting nonlinear regression analysis in time-series studies of the health effects of air pollution. When the data to which the GAM are being applied have two characteristics—1) the estimated regression coefficients are small and 2) there exist confounding factors that are modeled using at least two nonparametric smooth functions—the default settings in the gam function of the S-Plus software package (version 3.4) do not assure convergence of its iterative estimation procedure and can provide biased estimates of regression coefficients and standard errors. This phenomenon has occurred in time-series analyses of contemporary data on air pollution and mortality. To evaluate the impact of default implementation of the gam software on published analyses, the authors reanalyzed data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) using three different methods: 1) Poisson regression with parametric nonlinear adjustments for confounding factors; 2) GAM with default convergence parameters; and 3) GAM with more stringent convergence parameters than the default settings. The authors found that pooled NMMAPS estimates were very similar under the first and third methods but were biased upward under the second method. Am J Epidemiol 2002;156:193–203.

Keywords: air pollution; algorithms; backfitting; generalized additive models; time series; Abbreviations: GAM, generalized additive model(s); GLM, generalized linear model(s); NMMAPS, National Morbidity, Mortality, and Air Pollution Study; PM10, particulate matter <10 µm in diameter.

Journal Article.  5939 words.  Illustrated.

Subjects: Public Health and Epidemiology

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