Instead, the Mann-Whitney U test was used to evaluate between-groups differences with inflation of family-wise type I error being controlled using Bonferroni corrections.
We used linear mixed models and logistic regression models to evaluate between-group differences over time.
The Tukey-Kramer method was used for post hoc analysis of the ANOVA data to evaluate between-group differences.
Analysis of variance (ANOVA) and chi-square procedures will be used to evaluate between-group differences at baseline.
We used linear mixed models and accompanying effect sizes to evaluate between-group differences over time for interval level outcome variables (e.g. mean numbers and percentages).
Repeated measures ANOVAs were used to evaluate between-group differences in levels of cortisol, IL-6, and TNF-α over the course of the session.
The analysis used patient-level data from each study to evaluate between-group differences in the exposure-adjusted incidence rates of adverse events.
Analysis of variance for paired data was used to evaluate the within-group variations in all of the parameters, and analysis of variance for unpaired data to evaluate between-group differences after having calculated the percentage changes at each visit.
Student's t test and chi square analysis were used as appropriate to evaluate between-group differences in baseline characteristics, and changes of ABI before and after primary intervention.
ANOVA using SAS 9.2 statistical software (SAS Institute, NC, USA) was applied to evaluate between-group differences in the outcome variables, follow-up least significant differences (LSD) analysis verified these differences were significant.
ANCOVAs evaluating between-group differences in reported sleep problems showed a significant main effect of group.
