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Analysis of multisite intervention studies using generalized linear mixed models

Published online by Cambridge University Press:  21 June 2019

Nicole M. White Open the ORCID record for Nicole M. White [Opens in a new window]  and
Adrian G. Barnett Open the ORCID record for Adrian G. Barnett [Opens in a new window]
Nicole M. White*
Affiliation:
Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
Adrian G. Barnett
Affiliation:
Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
*
Author for correspondence: Dr Nicole White, Email: nm.white@qut.edu.au
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Abstract

Multisite intervention studies have become increasingly common in infection control, for example, looking for a change in hospital infection rates after a regional policy change. The design of these studies can take various forms, from pre–post observational studies to randomized trials, in which sites are randomly assigned to the intervention or in which the intervention is sequentially introduced to different sites over time. Data collected under these settings are clustered by hospital and/or ward, consist of repeated measurements and, in some cases, exhibit temporal and/or seasonal patterns. Failure to account for these features in data analysis could well result in biased estimates of intervention effectiveness and impact on the generalizability of model results.

Type
Review
Information
Infection Control & Hospital Epidemiology , Volume 40 , Issue 8 , August 2019 , pp. 910 - 917
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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