In this report, we present growth curve modeling (GCM) with landmark registration as an alternative statistical approach for the analysis of time series cortisol data. This approach addresses an often-ignored but critical source of variability in salivary cortisol analyses: individual and group differences in the time latency of post-stress peak concentrations. It allows for the simultaneous examination of cortisol changes before and after the peak while controlling for timing differences, and thus provides additional information that can help elucidate group differences in the underlying biological processes (e.g., intensity of response, regulatory capacity). We tested whether GCM with landmark registration is more sensitive than traditional statistical approaches (e.g., repeated measures ANOVA--rANOVA) in identifying sex differences in salivary cortisol responses to a psychosocial stressor (Trier Social Stress Test--TSST) in healthy adults (mean age 23). We used plasma ACTH measures as our "standard" and show that the new approach confirms in salivary cortisol the ACTH finding that males had longer peak latencies, higher post-stress peaks but a more intense post-peak decline. This finding would have been missed if only saliva cortisol was available and only more traditional analytic methods were used. This new approach may provide neuroendocrine researchers with a highly sensitive complementary tool to examine the dynamics of the cortisol response in a way that reduces risk of false negative findings when blood samples are not feasible.
Keywords: ACTH; HPA; growth curve modeling; salivary cortisol; sex differences.