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Georgia Salanti - One of the best experts on this subject based on the ideXlab platform.
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CINeMA: An approach for assessing confidence in the results of a Network Meta-Analysis
PLoS Medicine, 2020Co-Authors: Adriani Nikolakopoulou, Anna Chaimani, Theodoros Papakonstantinou, Matthias Egger, Julian Higgins, Cinzia Del Giovane, Georgia SalantiAbstract:Background: The evaluation of the credibility of results from a Meta-Analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from Network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. Methodology: CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from Network Meta-Analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. Conclusions: Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated Networks.
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Estimating the contribution of studies in Network Meta-Analysis: paths, flows and streams [version 1; referees: 2 approved, 1 approved with reservations]
F1000 Research Ltd, 2018Co-Authors: Theodoros Papakonstantinou, Anna Chaimani, Adriani Nikolakopoulou, Gerta Rücker, Guido Schwarzer, Matthias Egger, Georgia SalantiAbstract:In Network Meta-Analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the Network. The percentage contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from Network Meta-Analysis, is crucial in this context. We use ideas from graph theory to derive the percentage that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step Network Meta-Analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to percentage contributions based on the observation that the rows of H can be interpreted as flow Networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published Networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to Network Meta-Analysis methodology, which allows the consistent derivation of the percentage contributions of direct evidence from individual studies to Network treatment effects
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initial orthodontic alignment effectiveness with self ligating and conventional appliances a Network meta analysis in practice
American Journal of Orthodontics and Dentofacial Orthopedics, 2014Co-Authors: Nikolaos Pandis, Padhraig S Fleming, Loukia M Spineli, Georgia SalantiAbstract:Systematic reviews of well-designed trials constitute a high level of scientific evidence and are important for medical decision making. Meta-Analysis facilitates integration of the evidence using a transparent and systematic approach, leading to a broader interpretation of treatment effectiveness and safety than can be attained from individual studies. Traditional meta-analyses are limited to comparing just 2 interventions concurrently and cannot combine evidence concerning multiple treatments. A relatively recent extension of the traditional meta-analytical approach is Network Meta-Analysis, which allows, under certain assumptions, the quantitative synthesis of all evidence under a unified framework and across a Network of all eligible trials. Network Meta-Analysis combines evidence from direct and indirect information via common comparators; interventions can therefore be ranked in terms of the analyzed outcome. In this article, the Network Meta-Analysis approach is introduced in a nontechnical manner using a worked example on the treatment effectiveness of conventional and self-ligating appliances.
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indirect treatment comparison Network meta analysis study questionnaire to assess relevance and credibility to inform health care decision making an ispor amcp npc good practice task force report
Value in Health, 2014Co-Authors: Jeroen P. Jansen, Joseph C Cappelleri, Thomas A Trikalinos, Sherry Andes, Randa Eldessouki, Georgia SalantiAbstract:Abstract Despite the great realized or potential value of Network Meta-Analysis of randomized controlled trial evidence to inform health care decision making, many decision makers might not be familiar with these techniques. The Task Force developed a consensus-based 26-item questionnaire to help decision makers assess the relevance and credibility of indirect treatment comparisons and Network Meta-Analysis to help inform health care decision making. The relevance domain of the questionnaire (4 questions) calls for assessments about the applicability of Network Meta-Analysis results to the setting of interest to the decision maker. The remaining 22 questions belong to an overall credibility domain and pertain to assessments about whether the Network Meta-Analysis results provide a valid answer to the question they are designed to answer by examining 1) the used evidence base, 2) analysis methods, 3) reporting quality and transparency, 4) interpretation of findings, and 5) conflicts of interest. The questionnaire aims to help readers of Network Meta-Analysis opine about their confidence in the credibility and applicability of the results of a Network Meta-Analysis, and help make decision makers aware of the subtleties involved in the analysis of Networks of randomized trial evidence. It is anticipated that user feedback will permit periodic evaluation and modification of the questionnaire.
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Evaluating the quality of evidence from a Network Meta-Analysis
PLoS ONE, 2014Co-Authors: Georgia Salanti, Cinzia Del Giovane, D.m. Caldwell, Anna Chaimani, Julian P T HigginsAbstract:Systematic reviews that collate data about the relative effects of multiple interventions via Network Meta-Analysis are highly informative for decision-making purposes. A Network Meta-Analysis provides two types of findings for a specific outcome: the relative treatment effect for all pairwise comparisons, and a ranking of the treatments. It is important to consider the confidence with which these two types of results can enable clinicians, policy makers and patients to make informed decisions. We propose an approach to determining confidence in the output of a Network Meta-Analysis. Our proposed approach is based on methodology developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for pairwise meta-analyses. The suggested framework for evaluating a Network Meta-Analysis acknowledges (i) the key role of indirect comparisons (ii) the contributions of each piece of direct evidence to the Network Meta-Analysis estimates of effect size; (iii) the importance of the transitivity assumption to the validity of Network Meta-Analysis; and (iv) the possibility of disagreement between direct evidence and indirect evidence. We apply our proposed strategy to a systematic review comparing topical antibiotics without steroids for chronically discharging ears with underlying eardrum perforations. The proposed framework can be used to determine confidence in the results from a Network Meta-Analysis. Judgements about evidence from a Network Meta-Analysis can be different from those made about evidence from pairwise meta-analyses.
Jeroen P. Jansen - One of the best experts on this subject based on the ideXlab platform.
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indirect treatment comparison Network meta analysis study questionnaire to assess relevance and credibility to inform health care decision making an ispor amcp npc good practice task force report
Value in Health, 2014Co-Authors: Jeroen P. Jansen, Joseph C Cappelleri, Thomas A Trikalinos, Sherry Andes, Randa Eldessouki, Georgia SalantiAbstract:Abstract Despite the great realized or potential value of Network Meta-Analysis of randomized controlled trial evidence to inform health care decision making, many decision makers might not be familiar with these techniques. The Task Force developed a consensus-based 26-item questionnaire to help decision makers assess the relevance and credibility of indirect treatment comparisons and Network Meta-Analysis to help inform health care decision making. The relevance domain of the questionnaire (4 questions) calls for assessments about the applicability of Network Meta-Analysis results to the setting of interest to the decision maker. The remaining 22 questions belong to an overall credibility domain and pertain to assessments about whether the Network Meta-Analysis results provide a valid answer to the question they are designed to answer by examining 1) the used evidence base, 2) analysis methods, 3) reporting quality and transparency, 4) interpretation of findings, and 5) conflicts of interest. The questionnaire aims to help readers of Network Meta-Analysis opine about their confidence in the credibility and applicability of the results of a Network Meta-Analysis, and help make decision makers aware of the subtleties involved in the analysis of Networks of randomized trial evidence. It is anticipated that user feedback will permit periodic evaluation and modification of the questionnaire.
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Is Network Meta-Analysis as valid as standard pairwise Meta-Analysis? It all depends on the distribution of effect modifiers
LSE Research Online Documents on Economics, 2013Co-Authors: Jeroen P. Jansen, Huseyin NaciAbstract:Background In the last decade, Network Meta-Analysis of randomized controlled trials has been introduced as an extension of pairwise Meta-Analysis. The advantage of Network Meta-Analysis over standard pairwise Meta-Analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise meta-analyses are well understood, those concerning Network meta-analyses are perceived to be more complex and prone to misinterpretation. Discussion In this paper, we aim to provide a basic explanation when Network Meta-Analysis is as valid as pairwise Meta-Analysis. We focus on the primary role of effect modifiers, which are study and patient characteristics associated with treatment effects. Because Network Meta-Analysis includes different trials comparing different interventions, the distribution of effect modifiers cannot only vary across studies for a particular comparison (as with standard pairwise Meta-Analysis, causing heterogeneity), but also between comparisons (causing inconsistency). If there is an imbalance in the distribution of effect modifiers between different types of direct comparisons, the related indirect comparisons will be biased. If it can be assumed that this is not the case, Network Meta-Analysis is as valid as pairwise Meta-Analysis. Summary The validity of Network Meta-Analysis is based on the underlying assumption that there is no imbalance in the distribution of effect modifiers across the different types of direct treatment comparisons, regardless of the structure of the evidence Network.
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Network meta analysis of individual and aggregate level data
Research Synthesis Methods, 2012Co-Authors: Jeroen P. JansenAbstract:Network Meta-Analysis is often performed with aggregate-level data (AgD). A challenge in using AgD is that the association between a patient-level covariate and treatment effects at the study level may not reflect the individual-level effect modification. In this paper, non-linear Network Meta-Analysis models for combining individual patient data (IPD) and AgD are presented to reduce bias and uncertainty of direct and indirect treatment effects in the presence of heterogeneity. The first method uses the same model form for IPD and AgD. With the second method, the model for AgD is obtained by integrating an underlying IPD model over the joint within-study distribution of covariates, in line with the method by Jackson et al. for ecological inferences. With simulated examples, the models are illustrated. Having IPD for a subset of studies improves estimation of treatment effects in the presence of patient-level heterogeneity. Of the two proposed non-linear models for combining IPD and AgD, the second seems less affected by bias in situations with large treatment-by-patient-level-covariate interactions, probably at the cost of greater uncertainty. Additional studies are needed to better understand when one model is favorable over the other. For Network Meta-Analysis, it is recommended to use IPD when available. Copyright © 2012 John Wiley & Sons, Ltd.
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conducting indirect treatment comparison and Network meta analysis studies report of the ispor task force on indirect treatment comparisons good research practices part 2
Value in Health, 2011Co-Authors: David C Hoaglin, Jeroen P. Jansen, Neil Hawkins, David Scott, Robbin F Itzler, Joseph C Cappelleri, Cornelis Boersma, David M Thompson, Kay M Larholt, Mireya DiazAbstract:Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and Network Meta-Analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of Network Meta-Analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional Meta-Analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting Network meta-analyses (our use of this term includes most methods that involve Meta-Analysis in the context of a Network of evidence). We start with a discussion of strategies for developing Networks of evidence. Next we briefly review assumptions of Network Meta-Analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of Network Meta-Analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and Network Meta-Analysis. A further section discusses eight key areas for future research.
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Network meta analysis of parametric survival curves
Research Synthesis Methods, 2010Co-Authors: M J Ouwens, Zoe Philips, Jeroen P. JansenAbstract:To inform health-care decision-making, treatments are often compared by synthesizing results from a number of randomized controlled trials. The Meta-Analysis may not only be focused on a particular pairwise comparison, but can also include multiple treatment comparisons by means of Network Meta-Analysis. For time-to-event outcomes such as survival, pooling is typically based on the hazard ratio (HR). The proportional hazards assumption that underlies current approaches of evidence synthesis is not only often implausible, but can also have a huge impact on decisions based on differences in expected outcomes, such as cost-effectiveness analysis. The application of a constant HR implies the assumption that the treatment only has an effect on one characteristic of the survival distribution, while commonly used survival distributions, like the Weibull distribution, have both a shape and a scale parameter. Instead of using constant HRs, this paper proposes Meta-Analysis of treatment effects based on the shape and scale parameters of parametric survival curves. The model for Meta-Analysis is extended for Network Meta-Analysis and illustrated with an example. Copyright © 2011 John Wiley & Sons, Ltd.
Dimitris Mavridis - One of the best experts on this subject based on the ideXlab platform.
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Network meta analysis techniques for synthesizing prevention science evidence
Prevention Science, 2021Co-Authors: Georgios Seitidis, Stavros Nikolakopoulos, Emily A Hennessy, Emily E Tannersmith, Dimitris MavridisAbstract:Network Meta-Analysis is a popular statistical technique for synthesizing evidence from studies comparing multiple interventions. Benefits of Network Meta-Analysis, over more traditional pairwise Meta-Analysis approaches, include evaluating efficacy/safety of interventions within a single framework, increased precision, comparing pairs of interventions that have never been directly compared in a trial, and providing a hierarchy of interventions in terms of their effectiveness. Network Meta-Analysis is relatively underutilized in prevention science. This paper therefore presents a primer of Network Meta-Analysis for prevention scientists who wish to apply this method or to critically appraise evidence from publications using the method. We introduce the key concepts and assumptions of Network Meta-Analysis, namely, transitivity and consistency, and demonstrate their applicability to the field of prevention science. We then illustrate the method using a Network Meta-Analysis examining the comparative effectiveness of brief alcohol interventions for preventing hazardous drinking among college students. We provide data and code for all examples. Finally, we discuss considerations that are particularly relevant in Network meta-analyses in the field of prevention, such as including non-randomized evidence.
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living Network meta analysis compared with pairwise meta analysis in comparative effectiveness research empirical study
BMJ, 2018Co-Authors: Adriani Nikolakopoulou, Dimitris Mavridis, Andrea Cipriani, Toshi A Furukawa, Andrea C Tricco, Sharon E StrausAbstract:Abstract Objective To examine whether the continuous updating of Networks of prospectively planned randomised controlled trials (RCTs) (“living” Network Meta-Analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise Meta-Analysis. Design Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Data sources Database of Network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Eligibility criteria for study selection Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each Network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P Outcomes and analysis Cumulative pairwise and Network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the Network Meta-Analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and Network meta-analyses. Results 49 comparisons of interest from 44 Networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both Network and pairwise Meta-Analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only Network Meta-Analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living Network Meta-Analysis and 23 years with living pairwise Meta-Analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in Network Meta-Analysis. Conclusions In comparative effectiveness research, prospectively planned living Network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses.
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graphical tools for Network meta analysis in stata
PLOS ONE, 2013Co-Authors: Anna Chaimani, Julian P T Higgins, Dimitris Mavridis, Panagiota Spyridonos, Georgia SalantiAbstract:Network Meta-Analysis synthesizes direct and indirect evidence in a Network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness Network Meta-Analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the Network Meta-Analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the Network Meta-Analysis model and interpret its results.
Bhaskar Roy - One of the best experts on this subject based on the ideXlab platform.
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bleeding with vascular endothelial growth factor tyrosine kinase inhibitor a Network meta analysis
Critical Reviews in Oncology Hematology, 2021Co-Authors: Avash Das, Somnath Mahapatra, Dhrubajyoti Bandyopadhyay, Santanu Samanta, Sandipan Chakraborty, Lisa L Philpotts, Eiman Jahangir, Bhaskar RoyAbstract:Abstract Background Targeted therapies like vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs) are the first-choice treatment in several types of cancers. We aim to determine the comparative risk of bleeding events associated with the VEGFR-TKIs through a Network Meta-Analysis. Methods Published data search up to November 2018 reporting bleeding in cancer patients treated with VEGFR-TKIs was performed. The primary outcome was presence of hemorrhagic events at the end of the trial. Bleeding as a side-effect profile was examined for eleven VEGFR-TKIs (Apatinib, Brivanib, Cabozantinib, Lenvatinib, Motesanib, Nintedanib, Pazopanib, Regorafenib, Sorafenib, Sunitinib and Vandetanib). Network Meta-Analysis based on random effects model estimating Odds Ratio (OR) with 95 % confidence interval (CI), compared the risk of bleeding events among the VEGFR-TKIs with respect to placebo control conditions. Results Fifty Randomized Clinical Trials (RCTs) including 16,753 cancer patients were included in this analysis. Twenty studies compared VEGFR-TKIs with placebo, the remaining studies compared VEGFR-TKIs with the standard chemotherapeutic regimen. VEGFR-TKIs were associated with increased incidence of all-grade hemorrhagic events in comparison to control (standard chemotherapy and/or placebo) (OR = 1.79; 95 % CI 1.50–2.13, p-value Conclusion VEGR-TKIs, particularly Sunitinib and Regorafenib appear to be associated with increased risk of bleeding incidence. Trial registration number PROSPERO CRD42017056406.
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comparison of treatment options for depression in heart failure a Network meta analysis
Journal of Psychiatric Research, 2019Co-Authors: Avash Das, Guido Schwarzer, Dhrubajyoti Bandyopadhyay, Lisa L Philpotts, Bhaskar Roy, Michael G Silverman, Olivia Ziegler, Shirshendu Sinha, James A Blumenthal, Saumya DasAbstract:Abstract Background Depression independently predicts poor outcomes in heart failure (HF) patients, including increased mortality, morbidity and 30-day re-hospitalization. In this Network Meta-Analysis, we compared different interventions designed to treat depression in HF. Materials and methods Electronic searches were conducted using Ovid MEDLINE, EMBASE, CINAHL, Web of Science, and PsycINFO up to November 2016. Included randomized clinical trials (RCTs) compared interventions (Exercise therapy (ET), cognitive behavioral therapy (CBT) or antidepressant (AD) medications) for depression in heart failure patients. The primary outcome was change in depressive symptoms based on validated measures of depression. Network Meta-Analysis based on random effects model estimating standardized mean difference (SMD) with 95% confidence interval (CI), compared the effects of the 3 classes of interventions with respect to usual care or placebo control conditions. Results A total of 21 RCTs (including 4563 HF patients) reporting the effects of treating depression in HF patients were included in the analysis. In comparison to placebo or usual standard of care, ET (SMD -0.38; 95% CI -0.54 to −0.22) and CBT (SMD -0.29; 95% CI -0.58 to −0.01) were associated with reduction in depressive symptoms whereas AD (SMD -0.16; 95% CI -0.44 to 0.11) was less effective. Conclusions This Meta-Analysis is suggestive of therapeutic benefit of ET and CBT in comparison to usual standard of care in treating depression in HF patients. However, comparison among the three interventions was not conclusive. Future randomized clinical trials are warranted to compare the therapeutic effects of ET, CBT and AD in such patients.
Avash Das - One of the best experts on this subject based on the ideXlab platform.
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bleeding with vascular endothelial growth factor tyrosine kinase inhibitor a Network meta analysis
Critical Reviews in Oncology Hematology, 2021Co-Authors: Avash Das, Somnath Mahapatra, Dhrubajyoti Bandyopadhyay, Santanu Samanta, Sandipan Chakraborty, Lisa L Philpotts, Eiman Jahangir, Bhaskar RoyAbstract:Abstract Background Targeted therapies like vascular endothelial growth factor receptor tyrosine kinase inhibitors (VEGFR-TKIs) are the first-choice treatment in several types of cancers. We aim to determine the comparative risk of bleeding events associated with the VEGFR-TKIs through a Network Meta-Analysis. Methods Published data search up to November 2018 reporting bleeding in cancer patients treated with VEGFR-TKIs was performed. The primary outcome was presence of hemorrhagic events at the end of the trial. Bleeding as a side-effect profile was examined for eleven VEGFR-TKIs (Apatinib, Brivanib, Cabozantinib, Lenvatinib, Motesanib, Nintedanib, Pazopanib, Regorafenib, Sorafenib, Sunitinib and Vandetanib). Network Meta-Analysis based on random effects model estimating Odds Ratio (OR) with 95 % confidence interval (CI), compared the risk of bleeding events among the VEGFR-TKIs with respect to placebo control conditions. Results Fifty Randomized Clinical Trials (RCTs) including 16,753 cancer patients were included in this analysis. Twenty studies compared VEGFR-TKIs with placebo, the remaining studies compared VEGFR-TKIs with the standard chemotherapeutic regimen. VEGFR-TKIs were associated with increased incidence of all-grade hemorrhagic events in comparison to control (standard chemotherapy and/or placebo) (OR = 1.79; 95 % CI 1.50–2.13, p-value Conclusion VEGR-TKIs, particularly Sunitinib and Regorafenib appear to be associated with increased risk of bleeding incidence. Trial registration number PROSPERO CRD42017056406.
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comparison of treatment options for depression in heart failure a Network meta analysis
Journal of Psychiatric Research, 2019Co-Authors: Avash Das, Guido Schwarzer, Dhrubajyoti Bandyopadhyay, Lisa L Philpotts, Bhaskar Roy, Michael G Silverman, Olivia Ziegler, Shirshendu Sinha, James A Blumenthal, Saumya DasAbstract:Abstract Background Depression independently predicts poor outcomes in heart failure (HF) patients, including increased mortality, morbidity and 30-day re-hospitalization. In this Network Meta-Analysis, we compared different interventions designed to treat depression in HF. Materials and methods Electronic searches were conducted using Ovid MEDLINE, EMBASE, CINAHL, Web of Science, and PsycINFO up to November 2016. Included randomized clinical trials (RCTs) compared interventions (Exercise therapy (ET), cognitive behavioral therapy (CBT) or antidepressant (AD) medications) for depression in heart failure patients. The primary outcome was change in depressive symptoms based on validated measures of depression. Network Meta-Analysis based on random effects model estimating standardized mean difference (SMD) with 95% confidence interval (CI), compared the effects of the 3 classes of interventions with respect to usual care or placebo control conditions. Results A total of 21 RCTs (including 4563 HF patients) reporting the effects of treating depression in HF patients were included in the analysis. In comparison to placebo or usual standard of care, ET (SMD -0.38; 95% CI -0.54 to −0.22) and CBT (SMD -0.29; 95% CI -0.58 to −0.01) were associated with reduction in depressive symptoms whereas AD (SMD -0.16; 95% CI -0.44 to 0.11) was less effective. Conclusions This Meta-Analysis is suggestive of therapeutic benefit of ET and CBT in comparison to usual standard of care in treating depression in HF patients. However, comparison among the three interventions was not conclusive. Future randomized clinical trials are warranted to compare the therapeutic effects of ET, CBT and AD in such patients.
