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

Abstract

Cortisol measures often are used to examine variation in hypothalamic-pituitary-adrenal axis (HPA) activity as well as broader patterns of differential health. However, substantial within-individual variation renders single cortisol measurements unreliable as estimates for probing differences between individuals and groups. A standard practice to clarify between-individual differences involves collecting multiple samples from each participant and then deriving person-specific averages. By ignoring information about variation at between- and within-individual levels, this technique impedes cross-study comparison of results, ignores data useful for future study design, and hinders the analysis of cross-level interactions. This report describes how multilevel approaches can simultaneously model between- and within-individual variation in diurnal cortisol levels without using crude averages. We apply these models to data from children in Nepal (n=29, 11-15 samples per child), Mongolia (n=47, 8-12 samples per child) and the US (n=1269, 1-6 samples per child). Using the Nepal data, we show how an analysis of crude time-adjusted aggregates does not detect an association between aggressive behavior and cortisol levels, while a multilevel analysis does. More importantly, we argue that the 'roadmap' to variation generated by these multilevel models provides meaningful information about the predictive accuracy--not just statistical significance--of relationships between cortisol levels and individual-level variables, such as psychopathology, age, and gender. The 'roadmap' also facilitates comparison between the results from different studies and estimation of the necessary number of cortisol measurements for future investigations.