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      Comparative effectiveness of psychological treatments for depressive disorders in primary care: network meta-analysis

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          Abstract

          Background

          A variety of psychological interventions to treat depressive disorders have been developed and are used in primary care. In a systematic review, we compared the effectiveness of psychological treatments grouped by theoretical background, intensity of contact with the health care professional, and delivery mode for depressed patients in this setting.

          Methods

          Randomized trials comparing a psychological treatment with usual care, placebo, another psychological treatment, pharmacotherapy, or a combination treatment in adult depressed primary care patients were identified by database searches up to December 2013. We performed both conventional pairwise meta-analysis and network meta-analysis combining direct and indirect evidence. Outcome measures were response to treatment (primary outcome), remission of symptoms, post-treatment depression scores and study discontinuation.

          Results

          A total of 37 studies with 7,024 patients met the inclusion criteria. Among the psychological treatments investigated in at least 150 patients face-to-face cognitive behavioral therapy (CBT; OR 1.80; 95 % credible interval 1.35–2.39), face-to-face counselling and psychoeducation (1.65; 1.27–2.13), remote therapist lead CBT (1.87; 1.38–2.53), guided self-help CBT (1.68; 1.22–2.30) and no/minimal contact CBT (1.53; 1.07–2.17) were superior to usual care or placebo, but not face-to-face problem-solving therapy and face-to-face interpersonal therapy. There were no statistical differences between psychological treatments apart from face-to-face interpersonal psychotherapy being inferior to remote therapist-lead CBT (0.60; 0.37–0.95). Remote therapist-led (0.86; 0.21–3.67), guided self-help (0.93; 0.62–1.41) and no/minimal contact CBT (0.85; 0.54–1.36) had similar effects as face-to-face CBT.

          Conclusions

          The limited available evidence precludes a sufficiently reliable assessment of the comparative effectiveness of psychological treatments in depressed primary care patients. Findings suggest that psychological interventions with a cognitive behavioral approach are promising, and primarily indirect evidence indicates that it applies also when they are delivered with a reduced number of therapist contacts or remotely.

          Systematic review registration: 01KG1012 at http://www.gesundheitsforschung-bmbf.de/de/2852.php

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12875-015-0314-x) contains supplementary material, which is available to authorized users.

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          Most cited references28

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          Evidence Synthesis for Decision Making 4

          Inconsistency can be thought of as a conflict between “direct” evidence on a comparison between treatments B and C and “indirect” evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.
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            Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

            Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I 2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R 2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I 2, which we call . We also provide a multivariate H 2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I 2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd.
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              Evidence synthesis for decision making 3: heterogeneity--subgroups, meta-regression, bias, and bias-adjustment.

              In meta-analysis, between-study heterogeneity indicates the presence of effect-modifiers and has implications for the interpretation of results in cost-effectiveness analysis and decision making. A distinction is usually made between true variability in treatment effects due to variation in patient populations or settings and biases related to the way in which trials were conducted. Variability in relative treatment effects threatens the external validity of trial evidence and limits the ability to generalize from the results; imperfections in trial conduct represent threats to internal validity. We provide guidance on methods for meta-regression and bias-adjustment, in pairwise and network meta-analysis (including indirect comparisons), using illustrative examples. We argue that the predictive distribution of a treatment effect in a "new" trial may, in many cases, be more relevant to decision making than the distribution of the mean effect. Investigators should consider the relative contribution of true variability and random variation due to biases when considering their response to heterogeneity. In network meta-analyses, various types of meta-regression models are possible when trial-level effect-modifying covariates are present or suspected. We argue that a model with a single interaction term is the one most likely to be useful in a decision-making context. Illustrative examples of Bayesian meta-regression against a continuous covariate and meta-regression against "baseline" risk are provided. Annotated WinBUGS code is set out in an appendix.
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                Author and article information

                Contributors
                Klaus.Linde@tum.de
                ruecker@imbi.uni-freiburg.de#
                kirsten@sigterman.nl
                susanneohnsmann@gmx.de
                Karin.Meissner@med.uni-muenchen.de
                antonius.schneider@tum.de
                l.kriston@uke.uni-hamburg.de
                Journal
                BMC Fam Pract
                BMC Fam Pract
                BMC Family Practice
                BioMed Central (London )
                1471-2296
                19 August 2015
                19 August 2015
                2015
                : 16
                : 103
                Affiliations
                [ ]Institute of General Practice, Technische Universität München, Orleansstr 47, D-81667 Munich, Germany
                [ ]Institute for Medical Biometry and Statistics, Medical Center - University of Freiburg, Stefan-Meier-Str. 26, D-79104 Freiburg, Germany
                [ ]Institute of Medical Psychology, Ludwig-Maximilians-University Munich, Goethestr 31, D-80336 Munich, Germany
                [ ]Department of Medical Psychology, University Medical Center Hamburg-Eppendorf, Martinistr 52, D-20246 Hamburg, Germany
                Article
                314
                10.1186/s12875-015-0314-x
                4545315
                26286590
                8b6ac528-0dfb-456a-b35e-6fe5c83bfd11
                © Linde et al. 2015

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 13 January 2015
                : 28 July 2015
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Medicine
                Medicine

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