Joint synthesis of multiple correlated outcomes in networks of interventions

Network meta analysis to predict the efficacy of an approved treatment in a new indication

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

Conduct and reporting of multivariate network meta-analyses: a scoping review

OBJECTIVES

Combining multivariate and network meta-analysis methods simultaneously in a multivariate network meta-analysis (MVNMA) provides the methodological framework to analyze the largest amount of evidence relevant to decision-makers (i.e., from indirect evidence and correlated outcomes). The objectives of this scoping review were to summarize the characteristics of MVNMAs published in the health sciences literature and map the methodological guidance available for MVNMA.

STUDY DESIGN AND SETTING

We searched MEDLINE, Embase, and the Cumulative Index to Nursing and Allied Health Literature from inception to 28 August 2023, along with citations of included studies, for quantitative evidence syntheses that applied MVNMA and articles addressing MVNMA methods. Pairs of reviewers independently screened potentially eligible studies. Collected data included bibliographic, methodological, and analytical characteristics of included studies. We reported results as total numbers, frequencies, and percentages for categorical variables and medians and interquartile ranges for continuous variables that were not normally distributed.

RESULTS

After screening 1,075 titles and abstracts, and 112 full texts, we included 38 unique studies, of which, 10 were quantitative evidence syntheses that applied MVNMA and 28 were articles addressing MVNMA methods. Among the 10 MVNMAs, the first was published in 2013, four used studies identified from already published systematic reviews, and eight addressed pharmacological interventions, which were the most common interventions. They evaluated interventions for metastatic melanoma, colorectal cancer, prostate cancer, oral hygiene, disruptive behavior disorders, rheumatoid arthritis, narcolepsy, type 2 diabetes, and overactive bladder syndrome. Five MVNMAs analyzed two outcomes simultaneously, and four MVNMAs analyzed three outcomes simultaneously. Among the articles addressing MVNMA methods, the first was published in 2007 and the majority provided methodological frameworks for conducting MVNMAs (26/28, 93%). One study proposed criteria to standardize reporting of MVNMAs and two proposed items relevant to the quality assessment of MVNMAs. Study authors used data from 18 different illnesses to provide illustrative examples within their methodological guidance.

CONCLUSIONS

The application of MVNMA in the health sciences literature is uncommon. Many methodological frameworks are published; however, standardization and specific criteria to guide reporting and quality assessment are lacking. This overview of the current landscape may help inform future conduct of MVNMAs and research on MVNMA methods.

Twenty years of network meta-analysis: Continuing controversies and recent developments

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.

Multivariate Bayesian Arm-Based Network Meta-Analysis of Pharmacological Interventions for the Treatment of Acute Bipolar Mania in Adults

BACKGROUND

In a network meta-analysis (NMA), multiple treatments can be compared simultaneously by aggregating pieces of evidence from direct as well as indirect treatment comparisons in different randomized controlled trials (RCTs). Conventional NMA are performed using a normal approximation approach and can be applied for arm-level binary outcome data as well. This study aimed to estimate the treatment effects within a Bayesian framework using a binomial likelihood for a multivariate NMA model.

METHODS

The dataset consists of 57 RCTs comparing the effect of ten pharmacological drugs and a placebo for acute bipolar mania in adults. The binary outcomes of interest were treatment response and all-cause dropouts measured three weeks from the baseline. Binomial distribution was adopted for the number of events and the probability of event occurrence modeled on the logit scale. Jeffrey's Beta prior was considered for the heterogeneity and inconsistency of standard deviation (SD) parameters. Cholesky and spherical decomposition strategies were adopted for the between-study variance-covariance matrix. Deviance information criterion (DIC) indices were computed to determine the model fit. All results pertaining to Markov chain Monte Carlo simulations and all analyses were carried out in WinBUGS software.

RESULTS

The estimated common heterogeneity SDs were similar, and the DIC values did not provide any evidence for superiority between the two decomposition strategies. The correlation (95% credible interval) between the outcomes was estimated as -0.31 (-0.71, -0.02) and -0.37 (-0.73, -0.03) for the Cholesky and spherical decompositions, respectively. Gelman-Rubin convergence statistics were stable, and Monte Carlo errors for all the parameters were around 0.005. Overall, olanzapine, paliperidone, and quetiapine were both significantly more effective and acceptable than a placebo when both the study outcomes were considered simultaneously.

CONCLUSIONS

The findings favoring olanzapine, paliperidone, and quetiapine possess an excellent concordance with the one adopted in clinical practice, and the Canadian Network for Mood and Anxiety Treatments and Royal Australian and New Zealand College of Psychiatrists guidelines recommend these as first-line drugs for treating bipolar disorder.

Using individual participant data to improve network meta-analysis projects

A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics.

177Lu-PSMA Radioligand Therapy Is Favorable as Third-Line Treatment of Patients with Metastatic Castration-Resistant Prostate Cancer. A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

In this systematic review and network meta-analysis (NMA), we aimed to assess the benefits and harms of third-line (L3) treatments in randomized controlled trials (RCTs) of patients with metastatic castration-resistant prostate cancer (mCRPC). Two reviewers searched for publications from 1 January 2006 to 30 June 2021. The review analyzed seven RCTs that included 3958 patients and eight treatments. Treatment with prostate-specific membrane antigen (PSMA)-based radioligand therapy (PRLT) resulted in a 1.3-times-higher rate of median PSA decline ≥50% than treatment with abiraterone, enzalutamide, mitoxantrone, or cabazitaxel (p = 0.00001). The likelihood was 97.6% for PRLT to bring about the best PSA response, out of the examined treatments. PRLT resulted in a 1.1-times-higher six-month rate of median radiographic progression-free survival. Treatment with PRLT in the VISION trial resulted in 1.05-times-higher twelve-month median overall survival than L3 treatment with cabazitaxel in other RCTs. PRLT more often resulted in severe thrombocytopenia and less often in severe leukopenia than did cabazitaxel. In conclusion, for patients with mCRPC, L3 treatment with PRLT is highly effective and safe.

Classifying information-sharing methods

BACKGROUND

Sparse relative effectiveness evidence is a frequent problem in Health Technology Assessment (HTA). Where evidence directly pertaining to the decision problem is sparse, it may be feasible to expand the evidence-base to include studies that relate to the decision problem only indirectly: for instance, when there is no evidence on a comparator, evidence on other treatments of the same molecular class could be used; similarly, a decision on children may borrow-strength from evidence on adults. Usually, in HTA, such indirect evidence is either included by ignoring any differences ('lumping') or not included at all ('splitting'). However, a range of more sophisticated methods exists, primarily in the biostatistics literature. The objective of this study is to identify and classify the breadth of the available information-sharing methods.

METHODS

Forwards and backwards citation-mining techniques were used on a set of seminal papers on the topic of information-sharing. Papers were included if they specified (network) meta-analytic methods for combining information from distinct populations, interventions, outcomes or study-designs.

RESULTS

Overall, 89 papers were included. A plethora of evidence synthesis methods have been used for information-sharing. Most papers (n=79) described methods that shared information on relative treatment effects. Amongst these, there was a strong emphasis on methods for information-sharing across multiple outcomes (n=42) and treatments (n=25), with fewer papers focusing on study-designs (n=23) or populations (n=8). We categorise and discuss the methods under four 'core' relationships of information-sharing: functional, exchangeability-based, prior-based and multivariate relationships, and explain the assumptions made within each of these core approaches.

CONCLUSIONS

This study highlights the range of information-sharing methods available. These methods often impose more moderate assumptions than lumping or splitting. Hence, the degree of information-sharing that they impose could potentially be considered more appropriate. Our identification of four 'core' methods of information-sharing allows for an improved understanding of the assumptions underpinning the different methods. Further research is required to understand how the methods differ in terms of the strength of sharing they impose and the implications of this for health care decisions.

Exploiting relationships between outcomes in Bayesian multivariate network meta-analysis with an application to relapsing-remitting multiple sclerosis

In multivariate network meta-analysis (NMA), the piecemeal nature of the evidence base means that there may be treatment-outcome combinations for which no data is available. Most existing multivariate evidence synthesis models are either unable to estimate the missing treatment-outcome combinations, or can only do so under particularly strong assumptions, such as perfect between-study correlations between outcomes or constant effect size across outcomes. Many existing implementations are also limited to two treatments or two outcomes, or rely on model specification that is heavily tailored to the dimensions of the dataset. We present a Bayesian multivariate NMA model that estimates the missing treatment-outcome combinations via mappings between the population mean effects, while allowing the study-specific effects to be imperfectly correlated. The method is designed for aggregate-level data (rather than individual patient data) and is likely to be useful when modeling multiple sparsely reported outcomes, or when varying definitions of the same underlying outcome are adopted by different studies. We implement the model via a novel decomposition of the treatment effect variance, which can be specified efficiently for an arbitrary dataset given some basic assumptions regarding the correlation structure. The method is illustrated using data concerning the efficacy and liver-related safety of eight active treatments for relapsing-remitting multiple sclerosis. The results indicate that fingolimod and interferon beta-1b are the most efficacious treatments but also have some of the worst effects on liver safety. Dimethyl fumarate and glatiramer acetate perform reasonably on all of the efficacy and safety outcomes in the model.

A Bayesian multivariate meta-analysis of prevalence data

When conducting a meta-analysis involving prevalence data for an outcome with several subtypes, each of them is typically analyzed separately using a univariate meta-analysis model. Recently, multivariate meta-analysis models have been shown to correspond to a decrease in bias and variance for multiple correlated outcomes compared with univariate meta-analysis, when some studies only report a subset of the outcomes. In this article, we propose a novel Bayesian multivariate random effects model to account for the natural constraint that the prevalence of any given subtype cannot be larger than that of the overall prevalence. Extensive simulation studies show that this new model can reduce bias and variance when estimating subtype prevalences in the presence of missing data, compared with standard univariate and multivariate random effects models. The data from a rapid review on occupation and lower urinary tract symptoms by the Prevention of Lower Urinary Tract Symptoms Research Consortium are analyzed as a case study to estimate the prevalence of urinary incontinence and several incontinence subtypes among women in suspected high risk work environments.

The Kilim plot: A tool for visualizing network meta-analysis results for multiple outcomes

Network meta‐analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every‐day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision‐making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application.

Utilizing radar graphs in the visualization of simulation and estimation results in network meta-analysis

Traditional visualization in meta-analysis uses forest plots to illustrate the combined treatment effect, along with the respective results from primary trials. While the purpose of visualization is clear in the pairwise setting, additional treatments broaden the focus and extend the results to be illustrated in network meta-analysis. The complexity increases further in situations where all potential contrasts in the network are compared to a predefined fixed value of interest, such as the 95% coverage evaluated against the nominal value of 95% in simulation studies. We propose utilizing radar graphs to illustrate results from network meta-analysis in cases where the interest lies in the comparison of estimated results (after fitting a network meta-analysis in a specific data set) or a performance measure (simulation study) to a pre-defined fixed reference value. Accounting for the complex high-dimensional data structure, the general picture of the full network is captured at once without increasing the space needed for visualization. Especially in large simulation studies, where multiple scenarios need to be visually combined to gain an overview on different scenarios, this type of illustration facilitates the discussion of results. Further properties, such as the expected variation due to the Monte-Carlo error or the differentiation between directly and indirectly estimated treatment contrasts in simulation studies, as well as the indication of well-connected and sparsely connected treatments in an applied network meta-analysis, can additionally be included in the visualization. While we used the radar-graph mainly for a simulation study, other applications are suitable whenever relative contributions of treatment (contrasts) are of interest.

Extensions of the probabilistic ranking metrics of competing treatments in network meta-analysis to reflect clinically important relative differences on many outcomes

One of the key features of network meta-analysis is ranking of interventions according to outcomes of interest. Ranking metrics are prone to misinterpretation because of two limitations associated with the current ranking methods. First, differences in relative treatment effects might not be clinically important and this is not reflected in the ranking metrics. Second, there are no established methods to include several health outcomes in the ranking assessments. To address these two issues, we extended the P-score method to allow for multiple outcomes and modified it to measure the mean extent of certainty that a treatment is better than the competing treatments by a certain amount, for example, the minimum clinical important difference. We suggest to present the tradeoff between beneficial and harmful outcomes allowing stakeholders to consider how much adverse effect they are willing to tolerate for specific gains in efficacy. We used a published network of 212 trials comparing 15 antipsychotics and placebo using a random effects network meta-analysis model, focusing on three outcomes; reduction in symptoms of schizophrenia in a standardized scale, all-cause discontinuation, and weight gain.

A meta-analysis of procedures to change implicit measures

Using a novel technique known as network meta-analysis, we synthesized evidence from 492 studies (87,418 participants) to investigate the effectiveness of procedures in changing implicit measures, which we define as response biases on implicit tasks. We also evaluated these procedures' effects on explicit and behavioral measures. We found that implicit measures can be changed, but effects are often relatively weak (|ds| < .30). Most studies focused on producing short-term changes with brief, single-session manipulations. Procedures that associate sets of concepts, invoke goals or motivations, or tax mental resources changed implicit measures the most, whereas procedures that induced threat, affirmation, or specific moods/emotions changed implicit measures the least. Bias tests suggested that implicit effects could be inflated relative to their true population values. Procedures changed explicit measures less consistently and to a smaller degree than implicit measures and generally produced trivial changes in behavior. Finally, changes in implicit measures did not mediate changes in explicit measures or behavior. Our findings suggest that changes in implicit measures are possible, but those changes do not necessarily translate into changes in explicit measures or behavior. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Comparative efficacy and acceptability of antidepressants in the long-term treatment of major depression: protocol for a systematic review and networkmeta-analysis

INTRODUCTION

Pharmacotherapy plays an important role in the treatment of major depression. At the initiation of antidepressant treatment, both improvement of symptoms in the short term and relapse prevention in the long term should be taken into account. However, there is insufficient evidence regarding the efficacy and the acceptability of continuation/maintenance treatments and the relative efficacy/acceptability of antidepressants.

OBJECTIVE

We will conduct a pairwise meta-analysis and a network meta-analysis (NMA) to examine the relative efficacy, tolerability and acceptability of antidepressants in the long-term treatment of major depression.

METHODS AND ANALYSIS

We will include double-blind randomised controlled trials comparing any of the following antidepressants, which we included in our previous NMA of the acute treatment for major depression, with placebo or with another active drug for long-term treatment of major depression: agomelatine, amitriptyline, bupropion, citalopram, clomipramine, desvenlafaxine, duloxetine, escitalopram, fluoxetine, fluvoxamine, levomilnacipran, milnacipran, mirtazapine, nefazodone, paroxetine, reboxetine, sertraline, trazodone, venlafaxine, vilazodone and vortioxetine. Our primary outcomes will be sustained response and all-cause dropouts. We will include four types of designs that are used to investigate long-term treatment. We will conduct two main analyses. First, we will conduct a pairwise meta-analysis comparing all antidepressants versus placebo to investigate whether continuing antidepressants after achieving a positive response in the acute-phase treatment is beneficial and/or safe. Second, we will conduct an NMA to examine the comparative efficacy and acceptability of the drugs. We will use a novel approach that will combine the results of acute-phase treatment NMA with long-term treatment studies to include all related designs in the NMA. We will ensure the validity of combining different designs and our new approach by checking the distribution of important effect modifiers and consistency of network.

ETHICS AND DISSEMINATION

This study did not require ethical approval. We will disseminate our findings by publishing results in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42018114561; Pre-results.

Multivariate network meta-analysis to mitigate the effects of outcome reporting bias

Outcome reporting bias (ORB) is recognized as a threat to the validity of both pairwise and network meta-analysis (NMA). In recent years, multivariate meta-analytic methods have been proposed to reduce the impact of ORB in the pairwise setting. These methods have shown that multivariate meta-analysis can reduce bias and increase efficiency of pooled effect sizes. However, it is unknown whether multivariate NMA (MNMA) can similarly reduce the impact of ORB. Additionally, it is quite challenging to implement MNMA due to the fact that correlation between treatments and outcomes must be modeled; thus, the dimension of the covariance matrix and number of components to estimate grows quickly with the number of treatments and number of outcomes. To determine whether MNMA can reduce the effects of ORB on pooled treatment effect sizes, we present an extensive simulation study of Bayesian MNMA. Via simulation studies, we show that MNMA reduces the bias of pooled effect sizes under a variety of outcome missingness scenarios, including missing at random and missing not at random. Further, MNMA improves the precision of estimates, producing narrower credible intervals. We demonstrate the applicability of the approach via application of MNMA to a multi-treatment systematic review of randomized controlled trials of anti-depressants for the treatment of depression in older adults.

Systematic reviews and cancer research: a suggested stepwise approach

Systematic reviews, with or without meta-analysis, play an important role today in synthesizing cancer research and are frequently used to guide decision-making. However, there is now an increase in the number of systematic reviews on the same topic, thereby necessitating a systematic review of previous systematic reviews. With a focus on cancer, the purpose of this article is to provide a practical, stepwise approach for systematically reviewing the literature and publishing the results. This starts with the registration of a protocol for a systematic review of previous systematic reviews and ends with the publication of an original or updated systematic review, with or without meta-analysis, in a peer-reviewed journal. Future directions as well as potential limitations of the approach are also discussed. It is hoped that the stepwise approach presented in this article will be helpful to both producers and consumers of cancer-related systematic reviews and will contribute to the ultimate goal of preventing and treating cancer.

An improved method for bivariate meta-analysis when within-study correlations are unknown

Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are not required, the Riley method is applicable to a wide variety of evidence synthesis situations. However, the standard variance estimator of the Riley method is not entirely correct under many important settings. As a consequence, the coverage of a function of pooled estimates may not reach the nominal level even when the number of studies in the multivariate meta-analysis is large. In this paper, we improve the Riley method by proposing a robust variance estimator, which is asymptotically correct even when the model is misspecified (ie, when the likelihood function is incorrect). Simulation studies of a bivariate meta-analysis, in a variety of settings, show a function of pooled estimates has improved performance when using the proposed robust variance estimator. In terms of individual pooled estimates themselves, the standard variance estimator and robust variance estimator give similar results to the original method, with appropriate coverage. The proposed robust variance estimator performs well when the number of studies is relatively large. Therefore, we recommend the use of the robust method for meta-analyses with a relatively large number of studies (eg, m≥50). When the sample size is relatively small, we recommend the use of the robust method under the working independence assumption. We illustrate the proposed method through 2 meta-analyses.

Resolve conflicting rankings of outcomes in network meta-analysis: Partial ordering of treatments

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.

Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples

Organisations such as the National Institute for Health and Care Excellence require the synthesis of evidence from existing studies to inform their decisions—for example, about the best available treatments with respect to multiple efficacy and safety outcomes. However, relevant studies may not provide direct evidence about all the treatments or outcomes of interest. Multivariate and network meta-analysis methods provide a framework to address this, using correlated or indirect evidence from such studies alongside any direct evidence. In this article, the authors describe the key concepts and assumptions of these methods, outline how correlated and indirect evidence arises, and illustrate the contribution of such evidence in real clinical examples involving multiple outcomes and multiple treatments

A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However, the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (). We investigate the use of our proposed methods in the context of both a simulation study and a real example.

Bayesian mixed treatment comparisons meta-analysis for correlated outcomes subject to reporting bias

Many randomized controlled trials (RCTs) report more than one primary outcome. As a result, multivariate meta-analytic methods for the assimilation of treatment effects in systematic reviews of RCTs have received increasing attention in the literature. These methods show promise with respect to bias reduction and efficiency gain compared to univariate meta-analysis. However, most methods for multivariate meta-analysis have focused on pairwise treatment comparisons (i.e., when the number of treatments is two). Current methods for mixed treatment comparisons (MTC) meta-analysis (i.e., when the number of treatments is more than two) have focused on univariate or very recently, bivariate outcomes. To broaden their application, we propose a framework for MTC meta-analysis of multivariate (≥ 2) outcomes where the correlations among multivariate outcomes within- and between-studies are accounted for through copulas, and the joint modeling of multivariate random effects, respectively. We consider a Bayesian hierarchical model using Markov Chain Monte Carlo methods for estimation. An important feature of the proposed framework is that it allows for borrowing of information across correlated outcomes. We show via simulation that our approach reduces the impact of outcome reporting bias (ORB) in a variety of missing outcome scenarios. We apply the method to a systematic review of RCTs of pharmacological treatments for alcohol dependence, which tends to report multiple outcomes potentially subject to ORB.

Combining randomized and non-randomized evidence in network meta-analysis

Non-randomized studies aim to reveal whether or not interventions are effective in real-life clinical practice, and there is a growing interest in including such evidence in the decision-making process. We evaluate existing methodologies and present new approaches to using non-randomized evidence in a network meta-analysis of randomized controlled trials (RCTs) when the aim is to assess relative treatment effects. We first discuss how to assess compatibility between the two types of evidence. We then present and compare an array of alternative methods that allow the inclusion of non-randomized studies in a network meta-analysis of RCTs: the naïve data synthesis, the design-adjusted synthesis, the use of non-randomized evidence as prior information and the use of three-level hierarchical models. We apply some of the methods in two previously published clinical examples comparing percutaneous interventions for the treatment of coronary in-stent restenosis and antipsychotics in patients with schizophrenia. We discuss in depth the advantages and limitations of each method, and we conclude that the inclusion of real-world evidence from non-randomized studies has the potential to corroborate findings from RCTs, increase precision and enhance the decision-making process. Copyright © 2017 John Wiley & Sons, Ltd.

An overview of methods for network meta-analysis using individual participant data: when do benefits arise?

Network meta-analysis (NMA) is a common approach to summarizing relative treatment effects from randomized trials with different treatment comparisons. Most NMAs are based on published aggregate data (AD) and have limited possibilities for investigating the extent of network consistency and between-study heterogeneity. Given that individual participant data (IPD) are considered the gold standard in evidence synthesis, we explored statistical methods for IPD-NMA and investigated their potential advantages and limitations, compared with AD-NMA. We discuss several one-stage random-effects NMA models that account for within-trial imbalances, treatment effect modifiers, missing response data and longitudinal responses. We illustrate all models in a case study of 18 antidepressant trials with a continuous endpoint (the Hamilton Depression Score). All trials suffered from drop-out; missingness of longitudinal responses ranged from 21 to 41% after 6 weeks follow-up. Our results indicate that NMA based on IPD may lead to increased precision of estimated treatment effects. Furthermore, it can help to improve network consistency and explain between-study heterogeneity by adjusting for participant-level effect modifiers and adopting more advanced models for dealing with missing response data. We conclude that implementation of IPD-NMA should be considered when trials are affected by substantial drop-out rate, and when treatment effects are potentially influenced by participant-level covariates.

GetReal in network meta-analysis: a review of the methodology

Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd.

Meta-Analysis of a Complex Network of Non-Pharmacological Interventions: The Example of Femoral Neck Fracture

Background

Surgical interventions raise specific methodological issues in network meta-analysis (NMA). They are usually multi-component interventions resulting in complex networks of randomized controlled trials (RCTs), with multiple groups and sparse connections.

Purpose

To illustrate the applicability of the NMA in a complex network of surgical interventions and to prioritize the available interventions according to a clinically relevant outcome.

Methods

We considered RCTs of treatments for femoral neck fracture in adults. We searched CENTRAL, MEDLINE, EMBASE and ClinicalTrials.gov up to November 2015. Two reviewers independently selected trials, extracted data and used the Cochrane Collaboration’s tool for assessing the risk of bias. A group of orthopedic surgeons grouped similar but not identical interventions under the same node. We synthesized the network using a Bayesian network meta-analysis model. We derived posterior odds ratios (ORs) and 95% credible intervals (95% CrIs) for all possible pairwise comparisons. The primary outcome was all-cause revision surgery.

Results

Data from 27 trials were combined, for 4,186 participants (72% women, mean age 80 years, 95% displaced fractures). The median follow-up was 2 years. With hemiarthroplasty (HA) and total hip arthroplasty (THA) as a comparison, risk of surgical revision was significantly higher with the treatments unthreaded cervical osteosynthesis (OR 8.0 [95% CrI 3.6–15.5] and 5.9 [2.4–12.0], respectively), screw (9.4 [6.0–16.5] and 6.7 [3.9–13.6]) and plate (12.5 [5.8–23.8] and 7.8 [3.8–19.4]).

Conclusions

In older women with displaced femoral neck fractures, arthroplasty (HA and THA) is the most effective treatment in terms of risk of revision surgery.

Systematic Review Registration

PROSPERO no. CRD42013004218.

Level of Evidence

Network Meta-Analysis, Level 1.

Multivariate meta-analysis using individual participant data

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.