Methods for epidemiologic analyses of multiple exposures: a review and comparative study of maximum-likelihood, preliminary-testing, and empirical-Bayes regression

Stat Med. 1993 Apr 30;12(8):717-36. doi: 10.1002/sim.4780120802.


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.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Bayes Theorem*
  • Bias
  • Computer Simulation
  • Epidemiologic Methods*
  • Forecasting
  • Humans
  • Infant Mortality / trends
  • Infant, Newborn
  • Likelihood Functions*
  • Logistic Models
  • Models, Statistical*
  • Odds Ratio
  • Probability
  • Random Allocation
  • Regression Analysis
  • Risk Factors