Methods for epidemiologic analyses of multiple exposures: a review and comparative study of maximum-likelihood, preliminary-testing, and empirical-Bayes regression.
Many epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analysed 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 modelling methods can outperform these conventional approaches. I here review these methods and compare two hierarchical methods, empirical-Bayes regression and a variant I call 'semi-Bayes' regression, to full-model maximum likelihood and to model reduction by preliminary testing. I then present a simulation study of logistic-regression analysis of weak exposure effects to illustrate the type of accuracy gains one may expect from hierarchical methods. Finally, I compare the performance of the methods in a problem of predicting neonatal mortality rates. Based on the literature to date, I suggest 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 90024-1772.
D001499:Bayes Theorem; D015982:Bias; D003198:Computer Simulation; D004812:Epidemiologic Methods; D005544:Forecasting; D006801:Humans; D007226:Infant Mortality / Q000639:trends; D007231:Infant, Newborn; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D016017:Odds Ratio; D011336:Probability; D011897:Random Allocation; D012044:Regression Analysis; D012307:Risk Factors