title: Statistical analysis and application of quasi experiments to antimicrobial resistance intervention studies.
abstract: Quasi-experimental study designs are frequently used to assess interventions that aim to limit the emergence of antimicrobial-resistant pathogens. However, previous studies using these designs have often used suboptimal statistical methods, which may result in researchers making spurious conclusions. Methods used to analyze quasi-experimental data include 2-group tests, regression analysis, and time-series analysis, and they all have specific assumptions, data requirements, strengths, and limitations. An example of a hospital-based intervention to reduce methicillin-resistant Staphylococcus aureus infection rates and reduce overall length of stay is used to explore these methods.
authors.affiliation: Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
authors.forename: Michelle
authors.initials: M
authors.lastname: Shardell
authors.forename: Anthony D
authors.initials: AD
authors.lastname: Harris
authors.forename: Samer S
authors.initials: SS
authors.lastname: El-Kamary
authors.forename: Jon P
authors.initials: JP
authors.lastname: Furuno
authors.forename: Ram R
authors.initials: RR
authors.lastname: Miller
authors.forename: Eli N
authors.initials: EN
authors.lastname: Perencevich
mesh_terms: D035061:Control Groups; D003428:Cross Infection / Q000517:prevention & control*; D003627:Data Interpretation, Statistical; D024901:Drug Resistance, Multiple, Bacterial; D006801:Humans; D017053:Infection Control / Q000379:methods*; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012044:Regression Analysis; D012107:Research Design