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Preclinical behavioral neuroscience often uses choice paradigms to capture psychiatric symptoms. In particular, the subfield of operant research produces nested datasets with many discrete choices in a session. The standard analytic practice is to aggregate choice into a continuous variable and analyze using ANOVA or linear regression. However, choice data often have multiple interdependent outcomes of interest, violating an assumption of general linear models. The aim of the current study was to quantify the accuracy of linear mixed-effects regression (LMER) for analyzing data from a 4-choice operant task called the Rodent Gambling Task (RGT), which measures decision-making in the context of various manipulations (eg, brain injury). Prior analysis of RGT data from intact rats (Sham; n= 58) and brain-injured rats (TBI; n= 51) revealed five distinct decision-making phenotypes for this task. To generate …