Abstract
IntroductionThere is rising awareness that we need multidisciplinary approaches integrating psychological treatments for schizophrenia, but a comprehensive evidence based on their relative efficacy is lacking. We will conduct a network meta-analysis (NMA), integrating direct and indirect comparisons from randomised controlled trials (RCTs) to rank psychological treatments for schizophrenia according to their efficacy, acceptability and tolerability.Methods and analysisWe will include all RCTs comparing a psychological treatment aimed at positive symptoms of schizophrenia with another psychological intervention or with a no treatment condition (waiting-list and treatment as usual). We will include studies on adult patients with schizophrenia, excluding specific subpopulations (eg, first-episode patients or patients with psychiatric comorbidities). Primary outcome will be the change in positive symptoms on a published rating scale. Secondary outcomes will be acceptability (dropout), change in overall and negative symptoms of schizophrenia, response, relapse, adherence, depression, quality of life, functioning and adverse events. Published and unpublished studies will be sought through database searches, trial registries and websites. Study selection and data extraction will be conducted by at least two independent reviewers. We will conduct random-effects NMA to synthesise all evidences for each outcome and obtain a comprehensive ranking of all treatments. NMA will be conducted in Stata and R within a frequentist framework. The risk of bias in studies will be evaluated using the Cochrane Risk of Bias tool and the credibility of the evidence will be evaluated using an adaptation of the Grading of Recommendations Assessment, Development and Evaluation framework to NMA, recommended by the Cochrane guidance. Subgroup and sensitivity analyses will be conducted to assess the robustness of the findings.Ethics and disseminationNo ethical issues are foreseen. Results from this study will be published in peer-reviewed journals and presented at relevant conferences.PROSPERO registration numberCRD42017067795.
Funder
German Research Foundation (DFG) and the Technical University of Munich
European Union’s Horizon 2020 research and innovation programme
Cited by
8 articles.
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