Hierarchical regression for epidemiologic analyses of multiple exposures.
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.
Greenland
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Department of Epidemiology, UCLA School of Public Health.
D000465:Algorithms; D001499:Bayes Theorem; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D005544:Forecasting; D006801:Humans; D007226:Infant Mortality; D007231:Infant, Newborn; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D012306:Risk; D014481:United States / Q000453:epidemiology