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Bayesian multilevel modeling

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  • Yulia Marchenko

    (StataCorp)

Abstract

In multilevel or hierarchical data, which include longitudinal, cross-sectional, and repeated-measures data, observations belong to different groups. Groups may represent different levels of hierarchy such as hospitals, doctors nested within hospitals, and patients nested within doctors nested within hospitals. Multilevel models incorporate group-specific effects in the regression model and assume that they vary randomly across groups according to some a priori distribution, commonly a normal distribution. This assumption makes multilevel models natural candidates for Bayesian analysis. Bayesian multilevel models additionally assume that other model parameters such as regression coefficients and variance components — variances of group-specific effects — are also random. ​ In this presentation, I will discuss some of the advantages of Bayesian multilevel modeling over the classical frequentist estimation. I will cover some basic random-intercept and random-coefficients modeling using the bayes: mixed command. I will then demonstrate more advanced model fitting by using the new-in-Stata-17 multilevel syntax of the bayesmh command, including multivariate and nonlinear multilevel models.

Suggested Citation

  • Yulia Marchenko, 2022. "Bayesian multilevel modeling," London Stata Conference 2022 04, Stata Users Group.
    • Handle: RePEc:boc:lsug22:04
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