Hierarchical regression for epidemiologic analyses of multiple exposures.
Open Access
- 1 November 1994
- journal article
- research article
- Published by Environmental Health Perspectives in Environmental Health Perspectives
- Vol. 102 (suppl 8) , 33-39
- https://doi.org/10.1289/ehp.94102s833
Abstract
Many epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analyzed either by fitting a risk-regression model with all exposures forced in the model, or by using a preliminary-testing algorithm, such as stepwise regression, to produce a smaller model. Research indicates that hierarchical modeling methods can outperform these conventional approaches. These methods are reviewed and compared to two hierarchical methods, empirical-Bayes regression and a variant here called "semi-Bayes" regression, to full-model maximum likelihood and to model reduction by preliminary testing. The performance of the methods in a problem of predicting neonatal-mortality rates are compared. Based on the literature to date, it is suggested that hierarchical methods should become part of the standard approaches to multiple-exposure studies.Keywords
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