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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: [email protected]
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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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References

Harris, AD, Bradham, DD, Baumgarten, M, Zuckerman, IH, Fink, JC, Perencevich, EN. The use and interpretation of quasi-experimental studies in infectious diseases. Clin Infect Dis 2004;38:1586–1591.Google ScholarPubMed
Shadish, WR, Cook, TD, Campbell, DT. Experimental and Quasi-experimental Designs for Generalized Causal Inference. Boston, MA; Houghton Mifflin; 2002.Google Scholar
Shardell, M, Harris, A, El-Kamary, S, Furuno, J, Miller, R, Perencevich, E. Statistical analysis and application of quasi experiments to antimicrobial resistance intervention studies. Clin Infect Dis 2007;45:901–907.Google ScholarPubMed
Pierce, RA, Lessler, J, Milstone, AM. Expanding the statistical toolbox: analytic approaches for cohort studies with healthcare-associated infectious outcomes. Curr Opin Infect Dis 2015;28:384–391.CrossRefGoogle ScholarPubMed
Hall, L, Farrington, A, Mitchell, BG, et al. Researching effective approaches to cleaning in hospitals: protocol of the REACH study, a multi-site stepped-wedge randomised trial. Implement Sci 2015;11:44.CrossRefGoogle Scholar
Mitchell, B, Hall, L, White, N, et al. An environmental cleaning bundle and health-care-associated infections in hospitals (REACH): a multicentre, randomised trial. Lancet Infect Dis 2019;19:410–418.CrossRefGoogle ScholarPubMed
Anthony, CA, Peterson, RA, Polgreen, LA, Sewell, DK, Polgreen, PM. The seasonal variability in surgical site infections and the association with warmer weather: a population-based investigation. Infect Control Hosp Epidemiol 2017;38:809–816.CrossRefGoogle ScholarPubMed
Leekha, S, Diekema, DJ, Perencevich, EN. Seasonality of staphylococcal infections. Clin Microbiol Infect 2012;18:927–933.CrossRefGoogle ScholarPubMed
Furuya-Kanamori, L, McKenzie, SJ, Yakob, L, et al. Clostridium difficile infection seasonality: patterns across hemispheres and continents—a systematic review. PLoS One 2015;10:e0120730.CrossRefGoogle Scholar
Grayson, ML, Jarvie, LJ, Martin, R, et al. Significant reductions in methicillin-resistant Staphylococcus aureus bacteraemia and clinical isolates associated with a multisite, hand hygiene culture-change program and subsequent successful statewide roll-out. Med J Aust 2008;188:633–640.Google ScholarPubMed
Stroup, WW. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. Boca Raton, FL; CRC Press; 2016.Google Scholar
Barnett, AG, Page, K, Campbell, M, et al. Changes in healthcare-associated Staphylococcus aureus bloodstream infections after the introduction of a national hand hygiene initiative. Infect Control Hosp Epidemiol 2014;35:1029–1036.CrossRefGoogle ScholarPubMed
Graves, N, Weinhold, D, Tong, E. Effect of healthcare-acquired infection on length of hospital stay and cost. Infect Control Hosp Epidemiol 2007;28:280–292.CrossRefGoogle ScholarPubMed
Taggart, L, Leung, E, Muller, MP, Matukas, L, Daneman, N. Differential outcome of an antimicrobial stewardship audit and feedback program in two intensive care units: a controlled interrupted time series study. BMC Infect Dis 2015;15:480.CrossRefGoogle ScholarPubMed
Bates, D, Mächler, M, Bolker, B, Walker, S. Fitting linear mixed-effects models using lme4. J Statist Softw 2015;67:1–48.CrossRefGoogle Scholar
Sturtz, S, Ligges, U, Gelman, A. R2WinBUGS: A package for running WinBUGS from R. J Statist Softw 2005;12:1–16.CrossRefGoogle Scholar
Depaoli, S, Clifton, JP, Cobb, PR. Just another Gibbs sampler (JAGS) flexible software for MCMC implementation. J Educ Behav Statist 2016;41:628–649.CrossRefGoogle Scholar
Golding, N. greta: Simple and Scalable Statistical Modelling in R. 2018. https://greta-stats.org/Google Scholar
Hadfield, JD. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Statist Soft 2010;33:1–22.CrossRefGoogle Scholar
Martins, TG, Simpson, D, Lindgren, F, Rue, H. Bayesian computing with INLA: new features. Comput Statist Data Anal 2013;67:68–83.CrossRefGoogle Scholar
Lunn, D, Jackson, C, Best, N, Spiegelhalter, D, Thomas, A. The BUGS Book: A Practical Introduction to Bayesian Analysis. Boca Raton, FL; Chapman and Hall/CRC; 2012.Google Scholar
Fong, Y, Rue, H, Wakefield, J. Bayesian inference for generalized linear mixed models. Biostatistics 2010;11:397–412.CrossRefGoogle ScholarPubMed
Lavergne, C, Martinez, M, Trottier, C. Empirical model selection in generalized linear mixed effects models. Comput Statist 2008;23:99–109.CrossRefGoogle Scholar
Burnham, KP, Anderson, DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Method Res 2004;33:261–304.CrossRefGoogle Scholar
Raftery, AE. Bayesian model selection in social research. Sociol Methodol 1995:111–163.CrossRefGoogle Scholar
Hoeting, JA, Madigan, D, Raftery, AE, Volinsky, CT. Bayesian model averaging: a tutorial. Statist Sci 1999:14;382–401.Google Scholar
Wagenmakers, E-J, Farrell, S. AIC model selection using Akaike weights. Psychonomic Bull Rev 2004;11:192–196.CrossRefGoogle ScholarPubMed
Kürüm, E, Warren, JL, Schuck-Paim, C, et al. Bayesian model averaging with change points to assess the impact of vaccination and public health interventions. Epidemiology 2017;28:889–897.CrossRefGoogle ScholarPubMed
Ouwens, MJNM, Tan, FES, Berger, MPF. Local influence to detect influential data structures for generalized linear mixed models. Biometrics 2001;57:1166–1172.CrossRefGoogle ScholarPubMed
Pinho, LGB, Nobre, JS, Singer, JM. Cook’s distance for generalized linear mixed models. Comput Statist Data Anal 2015;82:126–136.CrossRefGoogle Scholar
Nieuwenhuis, R, Grotenhuis, MT, Pelzer, B. Influence. ME: tools for detecting influential data in mixed effects models. R Journal 2012;4:38–47.CrossRefGoogle Scholar
Greenland, S, Senn, SJ, Rothman, KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 2016;31:337–350.CrossRefGoogle ScholarPubMed
Davison, AC, Hinkley, DV. Bootstrap Methods and Their Application. New York; Cambridge University Press; 1997.CrossRefGoogle Scholar
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