As a preliminary step, the investigators will screen the experimental data for normality and for potential associations between outcome variables with demographic variables at baseline. In cases of strong deviations from normality, log or power transformations will be used as appropriate. For the subjective effects questionnaires, the peak effect will be identified for each subject and used as the dependent variable. The primary analytical technique will be linear mixed effects regression. The general linear mixed model (GLMM) is equivalent to repeated measures ANOVA when all subjects have complete data but automatically handles missing observations, producing unbiased estimates as long as the values are missing at random. This approach will allow use of the data from subjects who complete only part of the protocol or who have invalid measurements on some tests, greatly enhancing our power.