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American Journal of Epidemiology Advance Access published online on September 27, 2006
American Journal of Epidemiology, doi:10.1093/aje/kwj335
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.
Received November 7, 2005
Accepted May 8, 2006

SPECIAL ARTICLE

The Question of Nonlinearity in the Dose-Response Relation between Particulate Matter Air Pollution and Mortality: Can Akaike's Information Criterion be Trusted to Take the Right Turn?

Steven Roberts 1 * and Michael A. Martin 1

1 From the School of Finance and Applied Statistics, College of Business and Economics, Australian National University, Canberra, Australia

* To whom correspondence should be addressed.
Steven Roberts, E-mail: steven.roberts{at}anu.edu.au


  Abstract

The shape of the dose-response relation between particulate matter air pollution and mortality is crucial for public health assessment, and departures of this relation from linearity could have important regulatory consequences. A number of investigators have studied the shape of the particulate matter-mortality dose-response relation and concluded that the relation could be adequately described by a linear model. Some of these researchers examined the hypothesis of linearity by comparing Akaike's Information Criterion (AIC) values obtained under linear, piecewise linear, and spline alternative models. However, at the current time, the efficacy of the AIC in this context has not been assessed. The authors investigated AIC as a means of comparing competing dose-response models, using data from Cook County, Illinois, for the period 1987-2000. They found that if nonlinearities exist, the AIC is not always successful in detecting them. In a number of the scenarios considered, AIC was equivocal, picking the correct simulated dose-response model about half of the time. These findings suggest that further research into the shape of the dose-response relation using alternative model selection criteria may be warranted.

Keywords: air pollution; epidemiologic methods; models, statistical; mortality.
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