Inter-individual differences in performance impairment from sleep loss are substantial and consistent, as demonstrated and quantified here by means of the intraclass correlation coefficient (ICC) in two laboratory-based sleep deprivation studies. There is an urgent need, therefore, to consider inter-individual variability in biomathematical models of fatigue and performance, which currently treat individuals as being all the same. Traditional regression techniques do not handle inter-individual variability, but cutting-edge mixed-effects modeling techniques have recently become available to deal with inter-individual differences in the temporal dynamics of fatigue and performance. The standard two stage (STS), restricted maximum likelihood (REML), and non-linear mixed-effects modeling (NMEM) approaches to mixed-effects models are compared here using data from a chronic partial sleep deprivation experiment. Mixed-effects modeling can be incorporated in the two distinct steps (the direct and inverse problems) of biomathematical model development in order to deal with inter-individual differences. This paper demonstrates that inter-individual variability accounts for a large percentage of observed variance in neurobehavioral responses to sleep deprivation, and describes tools that model developers will need to produce a new generation of fatigue and performance models capable of incorporating inter-individual variability and useful for subject-specific prediction.