Data analytic plan
REGRESSION
Primary outcomes (i.e., peak or trough ratings post-drug administration) as a function of drug were examined with mixed-effects models for continuous outcomes, generalized estimating equations (GEE) for dichotomous outcomes, and multinomial logistic regression for nominal categorical outcomes (i.e., response option for study medication enjoyment was “yes,” “no,” and “no effect”). Chi-square analysis was conducted when GEE models did not converge. Drug conditions were compared using Tukey post-hoc tests. Analyses were replicated using area under the curve (AUC) analysis and including body mass index (BMI) as a covariate. Findings did not significantly change so only primary findings with peak effect analysis without controlling for BMI are reported. Sex did not moderate any study outcomes, so only main effects are reported. Main data analyses were conducted by an independent biostatistician who did not participate in outcome assessments. For all analysis SAS version 9.4 was used and alpha was set at 0.05, two tailed.