Endpoint analyses
CM
SECONDARY
The primary endpoint was analyzed on an intention-to-treat (ITT) principle using Pearson’s Chi-Squared test. On ITT analysis, all patients should be analyzed as part of the group to which they were randomly assigned. To perform this analysis, missing data were imputed using multiple imputations by chained equations. The predictive mean matching imputation method was implemented with 20 sets of imputations.The secondary endpoints were independently analyzed using a per-protocol analysis approach by means of linear mixed models. Using these models, each secondary endpoint is considered as dependent variable at each time point. They have the advantage to use all available data, thus all patients are included regardless they completed the trial or not. All models had a random intercept for patients (to account for within-person clustering of repeated observations) with unstructured covariance structure, and were adjusted for the following baseline characteristics: age (years), sex, years of education, CM duration, working status, and previous withdrawal. An interaction term between time and treatment group was included to test the differences between groups.Finally, we calculated whether the mindfulness add-on was associated to higher rates in achieving clinically meaningful improvement at HIT-6 score (i.e. change in score ≥ 6, specific for CM [Analyses were performed with Stata software, version IC 15.1 (StataCorp LLC). For all tests and models significance level was set at 