TY - JOUR
T1 - Estimating between- and within-individual variation in cortisol levels using multilevel models
AU - Hruschka, Daniel J.
AU - Kohrt, Brandon A.
AU - Worthman, Carol M.
N1 - Funding Information:
We greatly appreciate access to cortisol data from the Great Smoky Mountain study collected under the directorship of and in collaboration with E. Jane Costello. We gratefully acknowledge laboratory assistance provided by Katrina Trivers and Linda Cangelose and the field assistance of Holbrook Kohrt and Suren Baigal (Agency of the Prevention and Protection of Children from Abuse and Neglect, Ulaanbaatar, Mongolia) in Mongolia, and Richard Kunz and Indra Rai in Nepal. Finally, we appreciate the thoughtful comments of two anonymous reviewers as well as Thom McDade, Chris Kuzawa, Melissa Melby, Ryan Brown, and Amanda Thompson. Funding Sources: NIMH Agency #MH57761 and W.T. Grant Foundation (CMW). Department of Anthropology Summer Research Fund and Graduate School of Arts and Sciences Institute for Comparative and International Studies, Emory University (BAK).
PY - 2005/8
Y1 - 2005/8
N2 - 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.
AB - 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.
KW - Cortisol
KW - Hierarchical linear model
KW - Mixed effects
KW - Multilevel
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U2 - 10.1016/j.psyneuen.2005.03.002
DO - 10.1016/j.psyneuen.2005.03.002
M3 - Article
C2 - 15854786
AN - SCOPUS:18144413890
SN - 0306-4530
VL - 30
SP - 698
EP - 714
JO - Psychoneuroendocrinology
JF - Psychoneuroendocrinology
IS - 7
ER -