Analysis of multisite intervention studies using generalized linear mixed models.

Area of Science:

• Epidemiology
• Biostatistics
• Infection Control

Background:

• Multisite intervention studies are increasingly used to evaluate infection control policies.
• These studies often involve complex data structures, including clustering by hospital or ward and repeated measurements.
• Common study designs include pre-post observational studies and randomized trials with various allocation or introduction schemes.

Purpose of the Study:

• To highlight the importance of appropriate statistical methods for analyzing data from multisite intervention studies in infection control.
• To emphasize the potential biases that can arise from failing to account for data complexities.

Main Methods:

• Discusses the characteristics of data from multisite intervention studies: clustering, repeated measures, and potential temporal/seasonal patterns.
• Implies the need for statistical models that can handle these hierarchical and longitudinal data features.

Main Results:

• Failure to account for clustered and repeated measures can lead to biased estimates of intervention effectiveness.
• Ignoring temporal and seasonal patterns may also distort findings.
• Improper analysis impacts the generalizability and reliability of study conclusions.

Conclusions:

• Appropriate statistical analysis is crucial for accurate assessment of intervention effectiveness in multisite studies.
• Ignoring data complexities can compromise the validity of infection control research findings.
• Advanced statistical approaches are needed to ensure reliable results and informed policy decisions.