Three-dimensional evidence network plot system: covariate imbalances and effects in network meta-analysis explored using a new software tool

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.

Analysis of Mental Health Disease Trends Using BeGraph Software in Spanish Health Care Centers: Case Study

Background

In the era of big data, networks are becoming a popular factor in the field of data analysis. Networks are part of the main structure of BeGraph software, which is a 3D visualization application dedicated to the analysis of complex networks.

Objective

The main objective of this research was to visually analyze tendencies of mental health diseases in a region of Spain, using the BeGraph software, in order to make the most appropriate health-related decisions in each case.

Methods

For the study, a database was used with 13,531 records of patients with mental health disorders in three acute medical units from different health care complexes in a region of Spain. For the analysis, BeGraph software was applied. It is a web-based 3D visualization tool that allows the exploration and analysis of data through complex networks.

Results

The results obtained with the BeGraph software allowed us to determine the main disease in each of the health care complexes evaluated. We noted 6.50% (463/7118) of admissions involving unspecified paranoid schizophrenia at the University Clinic of Valladolid, 9.62% (397/4128) of admissions involving chronic paranoid schizophrenia with acute exacerbation at the Zamora Hospital, and 8.84% (202/2285) of admissions involving dysthymic disorder at the Rio Hortega Hospital in Valladolid.

Conclusions

The data analysis allowed us to focus on the main diseases detected in the health care complexes evaluated in order to analyze the behavior of disorders and help in diagnosis and treatment.

Charting the landscape of graphical displays for meta-analysis and systematic reviews: a comprehensive review, taxonomy, and feature analysis

BACKGROUND

Data-visualization methods are essential to explore and communicate meta-analytic data and results. With a large number of novel graphs proposed quite recently, a comprehensive, up-to-date overview of available graphing options for meta-analysis is unavailable.

METHODS

We applied a multi-tiered search strategy to find the meta-analytic graphs proposed and introduced so far. We checked more than 150 retrievable textbooks on research synthesis methodology cover to cover, six different software programs regularly used for meta-analysis, and the entire content of two leading journals on research synthesis. In addition, we conducted Google Scholar and Google image searches and cited-reference searches of prior reviews of the topic. Retrieved graphs were categorized into a taxonomy encompassing 11 main classes, evaluated according to 24 graph-functionality features, and individually presented and described with explanatory vignettes.

RESULTS

We ascertained more than 200 different graphs and graph variants used to visualize meta-analytic data. One half of these have accrued within the past 10 years alone. The most prevalent classes were graphs for network meta-analysis (45 displays), graphs showing combined effect(s) only (26), funnel plot-like displays (24), displays showing more than one outcome per study (19), robustness, outlier and influence diagnostics (15), study selection and p-value based displays (15), and forest plot-like displays (14). The majority of graphs (130, 62.5%) possessed a unique combination of graph features.

CONCLUSIONS

The rich and diverse set of available meta-analytic graphs offers a variety of options to display many different aspects of meta-analyses. This comprehensive overview of available graphs allows researchers to make better-informed decisions on which graphs suit their needs and therefore facilitates using the meta-analytic tool kit of graphs to its full potential. It also constitutes a roadmap for a goal-driven development of further graphical displays for research synthesis.

MetaInsight: An interactive web-based tool for analyzing, interrogating, and visualizing network meta-analyses using R-shiny and netmeta

Background

Network meta‐analysis (NMA) is a powerful analysis method used to identify the best treatments for a condition and is used extensively by health care decision makers. Although software routines exist for conducting NMA, they require considerable statistical programming expertise to use, which limits the number of researchers able to conduct such analyses.

Objectives

To develop a web‐based tool allowing users with only standard internet browser software to be able to conduct NMAs using an intuitive “point and click” interface and present the results using visual plots.

Methods

Using the existing netmeta and Shiny packages for R to conduct the analyses, and to develop the user interface, we created the MetaInsight tool which is freely available to use via the web.

Results

A package was created for conducting NMA which satisfied our objectives, and this is described, and its application demonstrated, using an illustrative example.

Conclusions

We believe that many researchers will find our package helpful for facilitating NMA as well as allowing decision makers to scrutinize presented results visually and in real time. This will impact on the relevance of statistical analyses for health care decision making and sustainably increase capacity by empowering informed nonspecialists to be able to conduct more clinically relevant reviews. It is also hoped that others will be inspired to create similar tools for other advanced specialist analyses methods using the freely available technologies we have adopted.

A practical approach to predict expansion of evidence networks: a case study in treatment-naive advanced melanoma

Network meta-analysis (NMA) is a statistical method used to produce comparable estimates of efficacy across a range of treatments that may not be compared directly within any single trial. NMA feasibility is determined by the comparability of the data and presence of a connected network. In rapidly evolving treatment landscapes, evidence networks can change substantially in a short period of time. We investigate methods to determine the optimum time to conduct or update a NMA based on anticipated available evidence. We report the results of a systematic review conducted in treatment-naive advanced melanoma and compare networks of evidence available at retrospective, current, and prospective time points. For included publications, we compared the primary completion date of trials from clinical trials registries (CTRs) with the date of their first available publication to provide an estimate of publication lag. Using CTRs we were able to produce anticipated networks for future time points based on projected study completion dates and average publication lags which illustrated expansion and strengthening of the initial network. We found that over a snapshot of periods between 2015 and 2018, evidence networks in melanoma changed substantively, adding new comparators and increasing network connectedness. Searching CTRs for ongoing trials demonstrates it is possible to anticipate future networks at a certain time point. Armed with this information, sensible decisions can be made over when best to conduct or update a NMA. Incorporating new and upcoming interventions in a NMA enables presentation of a complete, up-to-date and evolving picture of the evidence.

Trends in comparative efficacy and safety of malaria control interventions for maternal and child health outcomes in Africa: a study protocol for a Bayesian network meta-regression exploring the effect of HIV and malaria endemicity spectrum

INTRODUCTION

Unprecedented global efforts to prevent malaria morbidity and mortality in sub-Saharan Africa have saved hundreds of thousands of lives across the continent in the last two decades. This study aims to determine how the comparative efficacy and safety of available malaria control interventions intended to improve maternal and child health outcomes have changed over time considering the varied epidemiological contexts on the continent.

METHODS

We will review all randomised controlled trials that investigated malaria control interventions in pregnant women in sub-Saharan Africa and were published between January 1980 and December 2018. We will subsequently use network meta-regression to estimate temporal trends in the relative and absolute efficacy and safety of Intermittent Preventive Treatments, Intermittent Screening and Treatments, Insecticide-treated bed nets, and their combinations, and predict their ranking according to their relative and absolute efficacy and safety over time. Our outcomes will include 12 maternal and 7 child mortality and morbidity outcomes, known to be associated with either malaria infection or control. We will use intention-to-treat analysis to derive our estimates and meta-regression to estimate temporal trends and the effect modification by HIV infection, malaria endemicity and Plasmodium falciparum resistance to sulfadoxine-pyrimethamine, while adjusting for multiple potential confounders via propensity score calibration.

PROSPERO REGISTRATION NUMBER

CRD42018095138.

Description of network meta-analysis geometry: A metrics design study

Background

The conduction and report of network meta-analysis (NMA), including the presentation of the network-plot, should be transparent. We aimed to propose metrics adapted from graph theory and social network-analysis literature to numerically describe NMA geometry.

Methods

A previous systematic review of NMAs of pharmacological interventions was performed. Data on the graph’s presentation were collected. Network-plots were reproduced using Gephi 0.9.1. Eleven geometric metrics were tested. The Spearman test for non-parametric correlation analyses and the Bland-Altman and Lin’s Concordance tests were performed (IBM SPSS Statistics 24.0).

Results

From the 477 identified NMAs only 167 graphs could be reproduced because they provided enough information on the plot characteristics. The median nodes and edges were 8 (IQR 6–11) and 10 (IQR 6–16), respectively, with 22 included studies (IQR 13–35). Metrics such as density (median 0.39, ranged 0.07–1.00), median thickness (2.0, IQR 1.0–3.0), percentages of common comparators (median 68%), and strong edges (median 53%) were found to contribute to the description of NMA geometry. Mean thickness, average weighted degree and average path length produced similar results than other metrics, but they can lead to misleading conclusions.

Conclusions

We suggest the incorporation of seven simple metrics to report NMA geometry. Editors and peer-reviews should ensure that guidelines for NMA report are strictly followed before publication.

Importance of assessing and adjusting for cross-study heterogeneity in network meta-analysis: a case study of psoriasis

Aim: The importance of adjusting for cross-study heterogeneity when conducting network meta-analyses (NMAs) was demonstrated using a case study of biologic therapies for moderate-to-severe plaque psoriasis. Methods: Bayesian NMAs were conducted for Psoriasis Area and Severity Index 90 response. Several covariates were considered to account for cross-trial differences: baseline risk (i.e., placebo response), prior biologic use, body weight, psoriasis duration, age, race and baseline Psoriasis Area and Severity Index score. Model fit was evaluated. Results: The baseline risk-adjusted NMA, which adjusts for multiple observed and unobserved effect modifiers, was associated with the best model fit. Lack of adjustment for cross-trial differences led to different clinical interpretations of findings. Conclusion: Failure to adjust for cross-trial differences in NMA can have important implications for clinical interpretations when studying the comparative efficacy of healthcare interventions.

Graphs of study contributions and covariate distributions for network meta-regression

Background

Meta‐regression results must be interpreted taking into account the range of covariate values of the contributing studies. Results based on interpolation or extrapolation may be unreliable. In network meta‐regression (NMR) models, which include covariates in network meta‐analyses, results are estimated using direct and indirect evidence; therefore, it may be unclear which studies and covariate values contribute to which result. We propose graphs to help understand which trials and covariate values contribute to each NMR result and to highlight extrapolation or interpolation.

Methods

We introduce methods to calculate the contribution that each trial and covariate value makes to each result and compare them with existing methods. We show how to construct graphs including a network covariate distribution diagram, covariate‐contribution plot, heat plot, contribution‐NMR plot, and heat‐NMR plot. We demonstrate the methods using a dataset with treatments for malaria using the covariate average age and a dataset of topical fluoride interventions for preventing dental caries using the covariate randomisation year.

Results

For the malaria dataset, no contributing trials had an average age between 7–25 years and therefore results were interpolated within this range. For the fluoride dataset, there are no contributing trials randomised between 1954–1959 for most comparisons therefore, within this range, results would be extrapolated.

Conclusions

Even in a fully connected network, an NMR result may be estimated from trials with a narrower covariate range than the range of the whole dataset. Calculating contributions and graphically displaying them aids interpretation of NMR result by highlighting extrapolated or interpolated results.