A Practical Guide to Mixed-Effects Models for Clustered Data in Pediatric Research

Area of Science:

• Pediatric research
• Biostatistics
• Clinical research methodology

Background:

• Clustered data, where observations are grouped within higher-level units (e.g., patients within hospitals), are common in pediatric research.
• Standard statistical methods assuming independence yield invalid standard errors and misleading inferences when applied to correlated clustered data.
• Mixed-effects models offer a robust solution by incorporating random effects to quantify and account for between-cluster variation.

Purpose of the Study:

• To introduce mixed-effects models as a practical analytical tool for clinician-researchers dealing with clustered data.
• To demonstrate the application and interpretation of linear mixed-effects models (LMM) for continuous outcomes and generalized linear mixed models (GLMM) for binary outcomes.
• To highlight the potential biases introduced by naive analyses that ignore data clustering.

Main Methods:

• Illustrative examples using simulated neonatal intensive care unit (NICU) data.
• Specification and interpretation of LMM and GLMM using the R statistical software.
• Comparison of mixed-effects model results against naive analyses that disregard clustering.

Main Results:

• Mixed-effects models effectively account for within-cluster correlation, providing valid statistical inference.
• Naive analyses ignoring clustering can lead to biased standard errors and incorrect conclusions.
• The direction and magnitude of bias from ignoring clustering depend on the specific data structure.

Conclusions:

• Mixed-effects models are a crucial tool for accurate analysis of clustered data in pediatric research.
• Clinician-researchers should utilize mixed-effects models to avoid misleading conclusions from correlated data.
• Proper reporting of clustered data analyses is essential for scientific transparency and reproducibility.