Analytical methods to detect within-individual changes in discounting

Abstract

Discounting, as a quantifiable measure of impulsivity, is often estimated within individuals via nonlinear regression. Here, we describe how to directly estimate within-individual change of the discounting parameter between 2 conditions and subsequently statistically test for that change using the discounting data from a single individual. To date, there has been no systematic description of how to conduct such an analysis. Employing the method allows investigators and clinicians to evaluate whether a single individual has statistically changed the way he or she discounts between 2 conditions (e.g., in the absence or presence of a pharmacologic, different time points, different rewards, etc.). We further describe a meta-analytic approach for combining estimated changes in discounting from individuals in a sample to make population inference. By providing more precise population estimates, this approach increases statistical power over traditional analytic methods.