For continuous data, we compared the mean score between the two groups to give a mean difference (MD). This is presented with 95% confidence intervals (CIs). When there were different outcome measures used on the same construct, we estimated the standardized MD (SMD). We calculated standardized mean differences (SMDs) using end-scores on the basis of end of treatment results and follow-up data, respectively. If the trials did not report means and standard deviations but reported other values such as t-tests and P values, we tried to transform these into standard deviations. Dichotomous data were summarized as Odds ratios (OR) with 95% CIs. We calculated study estimates on the basis of end of treatment results and did separate analyses for different therapies as well as for follow-up data. Whenever there were incomplete reports or missing data on outcomes stated as having been assessed, we contacted the trial authors.

We performed the statistical analysis according to recommendations in the latest version of the Cochrane Handbook for Systematic Reviews of Interventions [16]. Meta-analyses were carried out even if there was concern about heterogeneity. If the heterogeneity was very high in the meta-analysis, we downgraded the quality by using the GRADE tool [15]. When carrying out the meta-analysis, we used the inverse variance method to give more precise estimates from trials with less variance (mostly larger trials) more weight. We used the random-effects model for meta-analysis since we expected some degree of clinical heterogeneity to be present in most cases and the fixed effect model when presenting singe trial results. If data pooling seemed feasible, we pooled the primary trials effects and calculated their 95% CIs.

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