NDLI: Additive versus multiplicative models in ecologic regression

Content Provider	Springer Nature Link
Author	Thompson, W. Douglas Wartenberg, Daniel
Copyright Year	2007
Abstract	Much research in environmental epidemiology relies on aggregate-level information on exposure to potentially toxic substances and on relevant covariates. We compare the use of additive (linear) and multiplicative (log-linear) regression models for the analysis of such data. We illustrate how both additive and multiplicative models can be fit to aggregate-level data sets in which disease incidence is the dependent variable, and contrast these results with similar models fitted to individual-level data. We find (1) that for aggregate-level data, multiplicative models are more likely than additive models to introduce bias into the estimation of rates, an effect not found with individual-level data; and (2) that under many circumstances multiplicative models reduce the precision of the estimates, an effect also not found in individual-level models. For both additive and multiplicative models of aggregate-level data, we find that, in the presence of covariates, narrow confidence interval are obtained only when two or more antecedent factors are strongly related to the measured covariate and/or the exposure of primary substantive interest. We conclude that the equivalency of fitting additive versus multiplicative models in studies with individual-level binary data does not carry over to studies that analyze aggregate-level information. For aggregate data, we strongly recommend use of additive models.
Starting Page	635
Ending Page	646
Page Count	12
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	21
Issue Number	5
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-05-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Numerical and Computational Methods in Engineering Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences Probability Theory and Stochastic Processes Math. Applications in Geosciences Math. Application in Environmental Science
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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