Mixed-Model Regression Analysis and Dealing with Interindividual Differences

This chapter considers mixed-model regression analysis, which is a specific technique for analyzing longitudinal data that properly deals with within- and between-subjects variance. The term ‘‘mixed model’’ refers to the inclusion of both fixed effects, which are model components used to define systematic relationships such as overall changes over time and/ or experimentally induced group differences; and random effects, which account for variability among subjects around the systematic relationships captured by the fixed effects. To illustrate how the mixed-model regression approach can help analyze longitudinal data with large inter-individual differences, the psychomotor vigilance data is considered from an experiment involving 88 h of total sleep deprivation, during which subjects received either sustained low-dose caffeine or placebo. The traditional repeated-measures analysis of variance (ANOVA) is applied, and it is shown that that this method is not robust against systematic interindividual variability. The data are then reanalyzed using linear mixed-model regression analysis in order to properly take into account the interindividual differences. The study concludes with an application of nonlinear mixed-model regression analysis of the data at hand, to demonstrate the considerable potential of this relatively novel statistical approach.