Abstract

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Highlights

  • INTRODUCTIONIn this article we describe generalized linear latent and mixed models (GLLAMMs) and illustrate their potential in epidemiology

  • In this article we describe generalized linear latent and mixed models (GLLAMMs) and illustrate their potential in epidemiology.We begin by briefly describing ‘generalized linear models’ [1] which encompass common epidemiological tools such as linear regression, dichotomous logistic regression and Poisson regression

  • GLLAMMs provide five extensions to generalized linear mixed models: 1. Multilevel factor structures

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Summary

INTRODUCTION

In this article we describe generalized linear latent and mixed models (GLLAMMs) and illustrate their potential in epidemiology. A crucial assumption of generalized linear models is that the responses of different units i are independent given the covariates xi This assumption is often unrealistic since data are frequently of a multilevel nature with units i nested in clusters j. The combined effect of all unobserved cluster-level covariates is modeled by including random effects eta(m2j) in the linear predictor which take on the same value for all units in the same cluster. Η0(2j) is a random intercept, allowing the overall level of the linear predictor to vary between clusters j over and above the variability explained by the covariates xij. GLLAMMs provide five extensions to generalized linear mixed models (where we refer to η as latent variables, including random effects, factors, etc.):

Responses of mixed types
3: Abuse of several antibiotics
DISCUSSION
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