Estimating between- and within-individual variation in cortisol levels using multilevel models

Area of Science:

• Endocrinology
• Psychoneuroendocrinology
• Biostatistics

Background:

• Cortisol measurements are vital for understanding hypothalamic-pituitary-adrenal (HPA) axis activity and health variations.
• Single cortisol measurements are unreliable due to significant within-individual variability, complicating group comparisons.
• Current methods averaging multiple samples overlook crucial between- and within-individual variation data.

Purpose of the Study:

• To introduce and apply multilevel modeling for simultaneous analysis of between- and within-individual cortisol variation.
• To demonstrate the limitations of traditional averaging methods in capturing cortisol dynamics.
• To highlight the utility of multilevel models in understanding cortisol's relationship with individual characteristics.

Main Methods:

• Utilized multilevel modeling to analyze diurnal cortisol levels, accounting for both between- and within-individual variance.
• Applied models to diverse datasets from children in Nepal, Mongolia, and the US with varying sample sizes.
• Compared multilevel analysis with traditional aggregate analysis using Nepal data.

Main Results:

• Multilevel analysis detected an association between aggressive behavior and cortisol levels in Nepal children, missed by aggregate analysis.
• The 'roadmap' from multilevel models offers insights into predictive accuracy beyond statistical significance.
• Models facilitate cross-study comparisons and inform optimal cortisol sampling strategies for future research.

Conclusions:

• Multilevel modeling provides a robust framework for analyzing cortisol diurnal rhythms and individual variability.
• This approach enhances the understanding of cortisol's role in health, psychopathology, and demographic factors.
• Accurate modeling of cortisol variation is crucial for reliable scientific inquiry and clinical application.