Search

Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta-analysis (NMA) is an active area of research. We extend this work to include the contributions of paths of evidence. We present a general framework, based on the path-design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have negative coefficients. We show that two known approaches, called shortestpath and randomwalk, are special solutions of this equation, and both meet an optimization criterion, as they minimize the sum of absolute path contributions. In general, there is an infinite set of solutions, which can be identified using the generalized inverse (Moore-Penrose pseudoinverse). We consider two further special approaches. For large networks we find that shortestpath is superior with respect to run time and variability, compared to the other approaches, and is thus recommended in practice. The path-weights framework also has the potential to answer more general research questions in NMA., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network., Results: We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments., Conclusions: The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias., (© 2024. The Author(s).)

Traditional pairwise meta-analysis (PMA) is a very useful method that pools evidence from one study design type if appropriate; its widespread use in nutrition research is an important phenomenon. Recently, a promising method for more advanced evidence-synthesis, called network meta-analysis (NMA), was introduced. NMA is an extension of PMA that enables simultaneous comparison of multiple interventions. NMA combines direct evidence (i.e., trials comparing 2 interventions directly) and indirect evidence (i.e., from a connected route via ≥1 comparators, e.g. placebo) in a network of studies. NMAs have the potential to advance knowledge in the field of nutrition as they provide insights that cannot be obtained by individual 2-arm randomized controlled trials or PMA. Thus, in this perspective paper, we aim to summarize the current (methodologic) status of published NMAs in nutrition research and emphasize advances and strengths in comparison with traditional PMA through specific examples, and highlight potential pitfalls and limitations. NMA is an emerging methodology in the field of nutrition research. A PubMed search identified only 23 nutrition research-related NMAs published since the inception of journals up to January 8, 2019 (61% of them published since 2017), compared with >5000 published PMAs. Moreover, we aim to highlight the scientific concepts and standards through the use of the following NMA example: "Which type of oils/solid fats offers the greatest impact on blood lipids?" In this regard, we discuss intervention definitions, transitivity/similarity, statistical methods, description and visualization of results, inconsistency, ranking, dissemination bias, assessing the certainty of evidence by Grading of Recommendations Assessment, Development and Evaluation, and reporting guidelines. We expect that rigorously conducted NMAs based on high-quality systematic reviews will become the new evidence synthesis benchmark in nutrition research. However, caution is warranted because abuse and misinterpretations of PMA and NMA findings could hamper the scientific field and possibly decision-making regarding public policy., (Copyright © American Society for Nutrition 2019.)

Importance: Posttraumatic stress disorder (PTSD) is a prevalent mental disorder, with a high risk of chronicity, comorbidity, and functional impairment; PTSD is complicated to treat, and the debate on the best treatment approach is ongoing., Objective: To examine comparative outcomes and acceptability of psychotherapeutic and pharmacological treatments and their combinations in adults with PTSD., Data Sources: Embase, MEDLINE, PsycINFO, Cochrane Controlled Trials Register, and PSYNDEX were searched for studies published from January 1, 1980, to February 28, 2018. Reference lists of included studies and of previously published guidelines and systematic reviews were also searched., Study Selection: Of 11 417 records identified, 12 published randomized clinical trials (RCTs) comprising 922 participants, contributing 23 direct comparisons between psychotherapeutic and pharmacological treatments or their combinations were included., Data Extraction and Synthesis: Standardized mean differences (SMDs) and odds ratios were aggregated using random-effects network and pairwise meta-analyses. Risk of bias and indirectness was rated for each study, and network confidence was rated using the Confidence in Network Meta-Analysis framework., Main Outcomes and Measures: The primary outcome was the comparative benefit between 2 treatment approaches to PTSD symptom improvement, and secondary outcome was the comparative acceptability of the treatment approaches, as indicated by patient dropout rates before treatment termination., Results: No treatment approach was found to be superior at the end of treatment (for all, 95% CI included 0). At the last follow-up, psychotherapeutic treatments showed greater benefit than pharmacological treatments in both network (SMD, -0.83; 95% CI, -1.59 to -0.07) and pairwise (SMD, -0.63; 95% CI, -1.18 to -0.09, 3 RCTs) meta-analyses. No difference was found between combined treatments and psychotherapeutic treatments at long-term follow-up, and combined treatments were associated with better outcomes than pharmacological treatments in the network meta-analysis (SMD, -0.96; 95% CI, -1.87 to -0.04), but not in the pairwise meta-analysis, which included 2 RCTs (SMD, -1.02; 95% CI, -2.77 to 0.72). No evidence was found for differential acceptability of the 3 treatment approaches., Conclusions and Relevance: These results suggest superiority of psychotherapeutic treatments over pharmacological treatments; network, but not pairwise, meta-analyses suggest superiority of combined treatments over pharmacological treatments in improving PTSD symptom severity in the long term. The scarcity of reported long-term findings hampers definite conclusions and demonstrates the need for robust evidence from large-scaled comparative trials providing long-term follow-up data.

The Mantel-Haenszel (MH) method has been used for decades to synthesize data obtained from studies that compare two interventions with respect to a binary outcome. It has been shown to perform better than the inverse-variance method or Peto's odds ratio when data is sparse. Network meta-analysis (NMA) is increasingly used to compare the safety of medical interventions, synthesizing, eg, data on mortality or serious adverse events. In this setting, sparse data occur often and yet there is to-date, no extension of the MH method for the case of NMA. In this paper, we fill this gap by presenting a MH-NMA method for odds ratios. Similarly to the pairwise MH method, we assume common treatment effects. We implement our approach in R, and we provide freely available easy-to-use routines. We illustrate our approach using data from two previously published networks. We compare our results to those obtained from three other approaches to NMA, namely, NMA with noncentral hypergeometric likelihood, an inverse-variance NMA, and a Bayesian NMA with a binomial likelihood. We also perform simulations to assess the performance of our method and compare it with alternative methods. We conclude that our MH-NMA method offers a reliable approach to the NMA of binary outcomes, especially in the case or sparse data, and when the assumption of methodological and clinical homogeneity is justifiable., (© 2019 John Wiley & Sons, Ltd.)

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., Competing Interests: No competing interests were disclosed.

Network meta-analysis has evolved into a core method for evidence synthesis in health care. In network meta-analysis, 3 or more treatments for a given medical condition are compared, based on a number of clinical studies, usually randomized controlled trials. Often, many different endpoints are investigated, related to different aspects of the patient's outcome, such as efficacy, safety, acceptability, or costs of a treatment. Different outcomes may lead to different rankings of the treatments. We use the existing theory of partially ordered sets and show how the relations between the treatments in a network meta-analysis can be illustrated by Hasse diagrams, that is, directed graphs showing the partial order relations, and by structured scatter plots and biplots., (Copyright © 2017 John Wiley & Sons, Ltd.)

In systematic reviews based on network meta-analysis, the network structure should be visualized. Network plots often have been drawn by hand using generic graphical software. A typical way of drawing networks, also implemented in statistical software for network meta-analysis, is a circular representation, often with many crossing lines. We use methods from graph theory in order to generate network plots in an automated way. We give a number of requirements for graph drawing and present an algorithm that fits prespecified ideal distances between the nodes representing the treatments. The method was implemented in the function netgraph of the R package netmeta and applied to a number of networks from the literature. We show that graph representations with a small number of crossing lines are often preferable to circular representations., (© 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.)

In a quantitative synthesis of studies via meta‐analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta‐analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data‐generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta‐analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency. [ABSTRACT FROM AUTHOR]

The Peto odds ratio is a well‐known effect measure in meta‐analysis of binary outcomes. For pairwise comparisons, the Peto odds ratio estimator can be severely biased in the situation of unbalanced sample sizes in the two treatment groups or large treatment effects. In this publication, we evaluate Peto odds ratio estimators in the setting of multi‐arm studies and in network meta‐analysis using illustrative examples. We observe that Peto odds ratio estimators in a multi‐arm study are inconsistent if the observed event probabilities are different or the sample sizes of treatment groups are unbalanced. The same problem emerges in network meta‐analysis including only two‐arm studies and translates to indirect comparisons of pairwise meta‐analyses. We conclude that the Peto odds ratio should not be used as effect measure in network meta‐analysis or indirect comparisons of pairwise meta‐analyses. [ABSTRACT FROM AUTHOR]

Network meta‐analysis is a method to combine evidence from randomized controlled trials (RCTs) that compare a number of different interventions for a given clinical condition. Usually, this requires a connected network. A possible approach to link a disconnected network is to add evidence from nonrandomized comparisons, using propensity score or matching‐adjusted indirect comparisons methods. However, nonrandomized comparisons may be associated with an unclear risk of bias. Schmitz et al. used single‐arm observational studies for bridging the gap between two disconnected networks of treatments for multiple myeloma. We present a reanalysis of these data using component network meta‐analysis (CNMA) models entirely based on RCTs, utilizing the fact that many of the treatments consisted of common treatment components occurring in both networks. We discuss forward and backward strategies for selecting appropriate CNMA models and compare the results to those obtained by Schmitz et al. using their matching method. CNMA models provided a good fit to the data and led to treatment rankings that were similar, though not fully equal to that obtained by Schmitz et al. We conclude that researchers encountering a disconnected network with treatments in different subnets having common components should consider a CNMA model. Such models, exclusively based on evidence from RCTs, are a promising alternative to matching approaches that require additional evidence from observational studies. CNMA models are implemented in the R package netmeta. [ABSTRACT FROM AUTHOR]

Objective: The aim of this paper was to provide a systematic review and update on the pharmacotherapy of social anxiety disorder (SAD), including the efficacy and tolerability of these agents, the ranking of interventions, and the grading of results by quality of evidence. Methods: The Common Mental Disorder Controlled Trial Register and two trial registries were searched for randomised controlled trials (RCTs) comparing any pharmacological intervention or placebo in the treatment of SAD. We performed a standard pairwise meta-analysis using a random effects model and carried out a network meta-analysis (NMA) using the statistical package, R. Quality of evidence was also assessed. Results: We included 67 RCTs in the review and 21 to 45 interventions in the NMA. Paroxetine was most effective in the reduction of symptom severity as compared to placebo. Superior response to treatment was also observed for paroxetine, brofaromine, bromazepam, clonazepam, escitalopram, fluvoxamine, phenelzine, and sertraline. Higher dropout rates were found for fluvoxamine. Brofaromine, escitalopram, fluvoxamine, paroxetine, pregabalin, sertraline, and venlafaxine performed worse in comparison to placebo for the outcome of dropouts due to adverse events. Olanzapine yielded a relatively high rank for treatment efficacy and buspirone the worse rank for dropouts due to any cause. Conclusion: The differences between drugs and placebo were small, apart from a significant reduction in symptom severity and response for paroxetine. We suggest paroxetine as a first-line treatment of SAD, with the consideration of future research on the drug olanzapine as well as brofaromine, bromazepam, clonazepam, escitalopram, fluvoxamine, phenelzine, and sertraline because we observed a response to treatment. [ABSTRACT FROM AUTHOR]

In network meta‐analysis (NMA), treatments can be complex interventions, for example, some treatments may be combinations of others or of common components. In standard NMA, all existing (single or combined) treatments are different nodes in the network. However, sometimes an alternative model is of interest that utilizes the information that some treatments are combinations of common components, called component network meta‐analysis (CNMA) model. The additive CNMA model assumes that the effect of a treatment combined of two components A and B is the sum of the effects of A and B, which is easily extended to treatments composed of more than two components. This implies that in comparisons equal components cancel out. Interaction CNMA models also allow interactions between the components. Bayesian analyses have been suggested. We report an implementation of CNMA models in the frequentist R package netmeta. All parameters are estimated using weighted least squares regression. We illustrate the application of CNMA models using an NMA of treatments for depression in primary care. Moreover, we show that these models can even be applied to disconnected networks, if the composite treatments in the subnetworks contain common components. [ABSTRACT FROM AUTHOR]

Systematic reviewers conducting pairwise meta-analyses sometimes encounter multi-arm studies. To include these studies, and to avoid a unit-of-analysis error, often two or more arms are combined or the control arm is split. In this tutorial, we present 5 different approaches that can be used. Particularly, we present a novel approach (method 4) that to the best of our knowledge has not been presented before. We demonstrate their application on 3 selected data sets, discuss their scope of application and their advantages and limitations, and give recommendations. [ABSTRACT FROM AUTHOR]

Network meta-analysis is a statistical method combining information from randomised trials that compare two or more treatments for a given medical condition. Consistent treatment effects are estimated for all possible treatment comparisons. For estimation, weighted least squares regression that in a natural way generalises standard pairwise meta-analysis can be used. Typically, as part of the network, multi-arm studies are found. In a multi-arm study, observed pairwise comparisons are correlated, which must be accounted for. To this aim, two methods have been proposed, a standard regression approach and a new approach coming from graph theory and based on contrast-based data (Rücker 2012). In the standard approach, the dimension of the design matrix is appropriately reduced until it is invertible ('reduce dimension'). In the alternative approach, the weights of comparisons coming from multi-arm studies are appropriately reduced ('reduce weights'). As it was unclear, to date, how these approaches are related to each other, we give a mathematical proof that both approaches lead to identical estimates. The 'reduce weights' approach can be interpreted as the construction of a network of independent two-arm studies, which is basically equivalent to the given network with multi-arm studies. Thus, a simple random-effects model is obtained, with one additional parameter for a common heterogeneity variance. This is applied to a systematic review in depression. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

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. Highlights • Question : What is the comparative efficacy of Antidepressants (AD), Exercise Training (ET), and Cognitive Behavioral Therapy (CBT) in treating depression in heart failure (HF) patients? • Findings : In this network meta-analysis, ET and CBT offers significant therapeutic benefits in comparison to usual standard of care in treating depression in HF patients. • Meaning : ET with/without CBT, appears to be a reasonable intervention to treat depression in HF. Large randomized clinical trials are warranted to compare the therapeutic effects of these three interventions in such patients. [ABSTRACT FROM AUTHOR]