Statistical Analysis and Application of Quasi Experiments to Antimicrobial Resistance Intervention Studies

• Published in: Clinical infectious diseases Vol. 45; no. 7; pp. 901 - 907
• Main Authors: Eliopoulos, George M., Shardell, Michelle, Harris, Anthony D., El-Kamary, Samer S., Furuno, Jon P., Miller, Ram R., Perencevich, Eli N.
• Format: Journal Article
• Language: English
• Published: Chicago, IL The University of Chicago Press 01-10-2007; University of Chicago Press; Oxford University Press
• Subjects: Antimicrobial Resistance; Applied statistics; Biological and medical sciences; Control Groups; Cross Infection - prevention & control; Data Interpretation, Statistical; Drug resistance; Drug Resistance, Multiple, Bacterial; Humans; Infection Control - methods; Infections; Infectious diseases; Intervention; Linear regression; Medical sciences; Modeling; Outcome Assessment (Health Care) - methods; Regression Analysis; Research Design; Staphylococcus aureus; Staphylococcus infections; Statistical analysis; Studies; Time series; Time series analysis; Time series models; Infection; Drug susceptibility test; Statistical analysis
• ISSN: 1058-4838; 1537-6591
• DOI: 10.1086/521255

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.