Bayesian regression in SAS software

Sheena G. Sullivan, Sander Greenland

Research output: Contribution to journalArticleResearchpeer-review

59 Citations (Scopus)

Abstract

Bayesian methods have been found to have clear utility in epidemiologic analyses involving sparse-data bias or considerable background information. Easily implemented methods for conducting Bayesian analyses by data augmentation have been previously described but remain in scant use. Thus, we provide guidance on how to do these analyses with ordinary regression software. We describe in detail and provide code for the implementation of data augmentation for Bayesian and semi-Bayes regression in SAS ® software, and illustrate their use in a real logistic-regression analysis. For comparison, the same model was fitted using the Markov-chain Monte Carlo (MCMC) procedure. The two methods required a similar number of steps and yielded similar results, although for the main example, data augmentation ran in about 0.5% of the time required for MCMC. We also provide online appendices with details and examples for conditional logistic, Poisson and Cox proportional-hazards regression.

Original languageEnglish
Article numberdys213
Pages (from-to)308-317
Number of pages10
JournalInternational Journal of Epidemiology
Volume42
Issue number1
DOIs
Publication statusPublished - Feb 2013
Externally publishedYes

Keywords

  • Bayesian methods
  • Data augmentation
  • Logistic models
  • Markov-chain monte carlo (MCMC)

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