Paula Fernández

Long-term outcomes of Acceptance and Commitment Therapy in drug-dependent female inmates: A randomized controlled trial

Abstract The general aim of this randomized controlled trial was to test the long-term efficacy of acceptance and commitment therapy (ACT) compared to a cognitive behavioural therapy (CBT) condition in the treatment of drug abuse. Participants were 37 polydrug incarcerated females assessed with Mini International Neuropsychiatric Interview, Addiction Severity Index-6, Anxiety Sensitivity Index (ASI) and Acceptance and Action Questionnaire II at pre, post, and at 6-, 12- and 18-months follow-ups. The mixed lineal model analyses showed reductions in drug abuse, ASI levels and avoidance repertoire in both conditions, without any differences between groups. However, the percentages of mental disorders were reduced only in ACT participants. At the 18-month follow-up, ACT was better than CBT in the maintaining of abstinence rates. This data support the incubation pattern showed in previous ACT studies. To conclude, the ACT intervention seems to be an adequate treatment option for addictive behaviours and co-occurring disorders in incarcerated women.

Analysis of Type I Error Rates of Univariate and Multivariate Procedures in Repeated Measures Designs

We compared the robustness of univariate and multivariate statistical procedures to control Type I error rates when the normality and homocedasticity assumptions were not fulfilled. The procedures we evaluated are the mixed model adjusted by means of the SAS Proc Mixed module, and Bootstrap-F approach, Brown–Forsythe multivariate approach, Welch–James multivariate approach, and Welch–James multivariate approach with robust estimators. The results suggest that the Kenward Roger, Brown–Forsythe, Welch–James, and Improved Generalized Aprroximate procedures satisfactorily kept Type I error rates within the nominal levels for both the main and interaction effects under most of the conditions assessed.

Effects of Modeling the Heterogeneity on Inferences Drawn from Multilevel Designs

This article uses Monte Carlo techniques to examine the effect of heterogeneity of variance in multilevel analyses in terms of relative bias, coverage probability, and root mean square error (RMSE). For all simulated data sets, the parameters were estimated using the restricted maximum-likelihood (REML) method both assuming homogeneity and incorporating heterogeneity into multilevel models. We find that (a) the estimates for the fixed parameters are unbiased, but the associated standard errors are frequently biased when heterogeneity is ignored; by contrast, the standard errors of the fixed effects are almost always accurate when heterogeneity is considered; (b) the estimates for the random parameters are slightly overestimated; (c) both the homogeneous and heterogeneous models produce standard errors of the variance component estimates that are underestimated; however, taking heterogeneity into account, the REML-estimations give correct estimates of the standard errors at the lowest level and lead to less underestimated standard errors at the highest level; and (d) from the RMSE point of view, REML accounting for heterogeneity outperforms REML assuming homogeneity; a considerable improvement has been particularly detected for the fixed parameters. Based on this, we conclude that the solution presented can be uniformly adopted. We illustrate the process using a real dataset.