Network meta-analysis involves combining both direct and indirect evidence in one model. Based on this information, we can estimate the (relative) effect of each included treatment. By adding indirect evidence, we also increase the precision of an effect size estimate, even when there is direct evidence for that specific comparison.
Full Answer
What is netmeta in R?
The netmeta package in R is based on a novel approach for network meta-analysis that follows the graph-theoretical methodology. This method exploits the analogy between treatment networks and electrical networks to construct the network meta-analysis model accounting for the correlated treatment effects in multi-arm trials.
How to use frequentist NMA in R using R netmeta?
FREQUENTIST NMA USING R “netmeta” PACKAGE Figure 7shows the flowchart for using the R package “netmeta” for NMA using the frequentist method. First, you must change the data format to the effect size data format, and set the variable names in accordance with the relevant function.
Is pcnetmetais fully comprehensive?
In contrast, pcnetmetais not designed to be fully comprehensive, but instead to provide a small set of functions that make the modeling process very simple for the user by leaving out many options.
What is the best outcome type for pcnetmetacan?
The gemtcpackage is currently the only package that can accommodate both input types. Binary outcomes, the most common in NMA literature [3], are handled by all three packages, and it is the only outcome type that pcnetmetacan handle.
How long does it take to do a meta-analysis?
They estimated it should take from 25 to 2,518 hours, with a mean total of 1,139 hours, to conduct a meta-analysis. Their estimate included 588 hours needed for search, retrieval, and creation of a database for the search results.
What is pairwise meta-analysis?
Pairwise meta-analysis (PW-MA) is a method that pools evidence from randomised controlled trials (RCTs) that compare the same two interventions. Network meta-analysis (NMA) extends this to multiple interventions, where each RCT compares two or more different interventions.
What is Frequentist network meta-analysis?
Network meta-analysis is used to compare three or more treatments for the same condition. Within a Bayesian framework, for each treatment the probability of being best, or, more general, the probability that it has a certain rank can be derived from the posterior distributions of all treatments.
What is meta analytic approach?
Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research. Typically, but not necessarily, the study is based on randomized, controlled clinical trials.
What does a pairwise comparison show?
Pairwise comparisons are methods for analyzing multiple population means in pairs to determine whether they are significantly different from one another.
How do you assess transitivity?
In principle, transitivity can be evaluated by comparing the distribution of effect modifiers across the different comparisons (Salanti 2012, Cipriani et al 2013, Jansen and Naci 2013). Imbalanced distributions would threaten the plausibility of the transitivity assumption and thus the validity of indirect comparison.
What is the difference between Bayesian and frequentist statistics?
Frequentist statistics never uses or calculates the probability of the hypothesis, while Bayesian uses probabilities of data and probabilities of both hypothesis. Frequentist methods do not demand construction of a prior and depend on the probabilities of observed and unobserved data.
How do you read sucra values?
SUCRA values range from 0 to 100%. The higher the SUCRA value, and the closer to 100%, the higher the likelihood that a therapy is in the top rank or one of the top ranks; the closer to 0 the SUCRA value, the more likely that a therapy is in the bottom rank, or one of the bottom ranks.
What is a meta-analysis vs systematic review?
A systematic review attempts to gather all available empirical research by using clearly defined, systematic methods to obtain answers to a specific question. A meta-analysis is the statistical process of analyzing and combining results from several similar studies.
What are the benefits of a meta-analysis?
Benefits of meta-analysis Through meta-analysis, researchers can combine smaller studies, essentially making them into one big study, which may help show an effect. Additionally, a meta-analysis can help increase the accuracy of the results. This is also because it is, in effect, increasing the size of the study.
Why are meta-analysis useful?
The major advantage of meta-analysis is that accumulation of evidence can improve the precision and accuracy of effect estimates and increase the statistical power to detect an effect. A further advantage of meta-analysis is that it facilitates the generalization of results to a larger population.
What is the purpose of meta-analysis in research?
Meta-analysis would be used for the following purposes: To establish statistical significance with studies that have conflicting results. To develop a more correct estimate of effect magnitude. To provide a more complex analysis of harms, safety data, and benefits.
Description
Network meta-analysis is a generalisation of pairwise meta-analysis that compares all pairs of treatments within a number of treatments for the same condition. The graph-theoretical approach for network meta-analysis uses methods that were originally developed in electrical network theory.
Details
Network meta-analysis using R package netmeta is described in detail in Schwarzer et al. (2015), Chapter 8.
Value
An object of class netmeta with corresponding print , summary, forest, and netrank functions. The object is a list containing the following components:
What is netmeta package?
The package netmetaprovides a comprehensive set of functions for conducting a NMA in a frequentist setting. The package employs graph theory methodology presented in [32]. Contrast-level summary data (e.g. log-odds ratio) are input, so all types of outcome data can be meta-analyzed in this package. The modeling process provides flexible options for the incorporation of heterogeneity and inconsistency in the estimation. A unique feature of this package is the netheatfunction, which employs a heatmap plot [20]for the detection of inconsistency.
What is pcnetmeta?
The package pcnetmetaprovides a small set of easy-to-use tools to conduct a Bayesian NMA for the simple case of binary data where inconsistency is disregarded. The package reads in arm-based summary data of binary outcomes and models the event rates (i.e. probabilities of success) in different treatments using multivariate Bayesian hierarchical mixed model approach [18]. The package interfaces with JAGS software to conduct MCMC sampling. Estimates of relative treatment effects such as relative risks (RR), risk difference (RD) or odds ratio (OR) can be calculated for any two treatments. This package can be used for the detection as well as incorporation of (common or differential) heterogeneity of event rates across trials; however, it does not provide any function for identifying or incorporating inconsistency in the analysis. Outputs include a confidence interval plot of the estimated event rates and posterior density plots.
What is gemtc in NMA?
The package gemtcprovides a comprehensive set of tools to perform NMA in a Bayesian setting. Arm- or contrast-level network data can be input of the four common outcome types (binary, continuous, count or survival). It models the relative effects (e.g., log-odds ratio) fitting a generalized linear model (GLM) under the Bayesian framework by linking to JAGS, OpenBUGS or WinBUGS as first described by Lu and Ades [7], and extended by others [6], [12], [28], [31]. Important features of this package include its ability to model heterogeneity and inconsistency [6], [12], [17]. It provides flexibility in modeling as users can specify different likelihood and link functions, priors for hyper parameters, and several Markov-Chain Monte-Carlo (MCMC) sampling options. Estimates of relative treatment effects can be plotted via forest plots and that of rank probabilities can be plotted via rankograms.
What is NMA in health care?
NMA enables investigators to compare the effects of multiple health care interventions including treatments that were not previously compared in head-to-head trials. Additionally, combining indirect and direct evidence can sometimes provide more precise estimates of treatment effects to support decision-making.
What software is used to perform NMA?
Frequentist models can be implemented using commercial programs such as SAS and STATA. Freely available Bayesian software programs such as OpenBUGS, WinBUGS, or JAGS can be used to conduct Bayesian NMA, but they require developing a program code (or modifying pre-existing codes) that can be quite involved. In addition, some of the plotting tools of interest to NMA researchers are not incorporated into these programs. The statistical software program R is freely available and popular among statisticians because it is open source, allowing for the implementation of new statistical methods almost instantaneously through the creation of packages. R interfaces with all three Bayesian software programs mentioned above to conduct network meta-analyses with the use of appropriate packages. The user is not required to program in OpenBUGS, WinBUGS or JAGS in order to implement these packages, minimizing the programming required of the user. By combining the functionality of a few packages, almost all desired outputs can be obtained in R.
What packages are used for NMA?
Recently, three packages, gemtc( http://cran.r-project.org/web/packages/gemtc/index.html), pcnetmeta(http://cran.r- project.org/web/packages/pcnetmeta/index.html), and netmeta(http://cran.r-project.org/web/packages/netmeta/index.html), have been developed specifically for network meta-analysis in the Renvironment, allowing users to perform NMA with minimal programming. At the time of writing (July 2014), these are the only packages developed specifically for performing NMA that we identified. Each can automatically generate and run the analysis model with minimal programming required by users. The first two packages perform the analysis under the Bayesian framework and the third performs under the frequentist framework.
What is NMA in R?
Network meta-analysis (NMA) – a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously – has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.
What is NMA in medical terminology?
Network meta-analysis (NMA), also called multiple treatment meta-analysis, or mixed treatment comparison, aims to synthesize the effect sizes of several studies that evaluate multiple interventions or treatments [1-4].
How does Bayesian method work?
The Bayesian method calculates the posterior probability that the research hypothesis is true by adding the information given in the present data (likelihood) to previously known information (prior probability or external information). Therefore, it can be said that the Bayesian method is a probabilistic approach, where the probability that the research hypothesis is true can be changed depending on the prior information [1,2].
Where is the 6Institute of Medical Biometry and Statistics?
6Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
What is the probability of significance of a frequentist method?
In contrast, the frequentist method calculates the probability of significance (in general, p-value is 0.05) or the 95% confidence interval (CI) for rejecting or accepting the research hypothesis when the present data is repeated infinitely based on a general statistical theory. Therefore, the frequentist method is unrelated to external information, and the probability that the research hypothesis is true within the present data (likelihood) is already specified, and it only determines whether or not to accept or reject it based on the significance level [1,2].
How to estimate treatment effects?
To estimate the treatment effects, network meta-analysis combines both direct (i.e. observed) and indirect evidence. This, however, is based on the assumption of transitivity. Transitivity is fulfilled when we can combine direct evidence of two comparisons to derive valid indirect evidence about a third one.
What is meta analysis in clinical trials?
W hen we perform meta-analyses of clinical trials or other types of intervention studies, we usually estimate the true effect size of one specific treatment. We include studies in which the same type of intervention was compared to similar control groups, for example a placebo. All else being equal, this allows to assess if a specific type of treatment is effective.
How to run meta regression in Gemtc?
To run a network meta-regression in {gemtc}, we have to follow similar steps as before, when we fitted a Bayesian network meta-analysis model without covariates . First, we need to set up our network using mtc.network. This time, however, we specify an additional argument called studies. This argument requires a data frame in which predictor information for each study is stored. The TherapyFormatsGeMTC data set includes an element study.info, which contains the risk of bias of each study.
What is therapy format data?
This data set is modeled after a real network meta-analysis assessing the effectiveness of different delivery formats of cognitive behavioral therapy for depression ( P. Cuijpers, Noma, et al. 2019). All included studies are randomized controlled trials in which the effect on depressive symptoms was measured at post-test. Effect sizes of included comparisons are expressed as the standardized mean difference (SMD) between the two analyzed conditions.
How long does it take to compute a nodesplit model?
Please be aware that the nodesplit model computation may take a long time, even up to several hours, depending on the complexity of your network.
Can network meta-analysis include indirect evidence?
Network meta-analysis can incorporate indirect evidence in a network, which is not possible in conventional meta-analysis. In pairwise meta-analyses, we can only pool direct evidence from comparisons which were actually included in a trial.
Synopsis
To conduct a meta-analysis of (contrast-based) effect size data (i.e. pre-calculated effect sizes of treatment comparisons along with their standard error), different data entry formats are needed for {netmeta} and {gemtc}.
Reshaping
In this vignette, we will reshape the TherapyFormats data set. This data set is part of {dmetar}, but can also be downloaded as an .rda file here.
Multi-Arm Trials
Since our data contains a multi-arm trial, we cannot yet use the generated data set in {gemtc} as is. For multi-arm trials, more than one effect size is calculated, and these effect sizes are usually correlated. Suppose a trial contains J = 3 conditions; A, B and C.
What is Netmeta package?
The netmeta package in R is based on a novel approach for network meta-analysis that follows the graph-theoretical methodology. This method exploits the analogy between treatment networks and electrical networks to construct the network meta-analysis model accounting for the correlated treatment effects in multi-arm trials. Fixed and random effects models have been implemented in netmeta; the latter is constructed under the assumption of a common heterogeneity across all comparisons. Additional outputs of the package are Q-statistics for heterogeneity and inconsistency, forest plots of the pooled treatments effects versus a common reference treatment and network diagrams. Also, the ‘netheat’ plot (developed by Krahn et al. 2013) has been implemented in the package; this is a graph that helps to identify pairwise comparisons that might be potential sources of important inconsistency in the network.
What is the MvMeta command?
The mvmeta command in STATA employs a recent approach to network meta-analysis that handles the different treatment comparisons appeared in studies as different outcomes. The command can perform fixed and random effects network meta-analysis assuming either a common or different between-study variances across comparisons. Both consistency and inconsistency models (the ‘design-by-treatment model’ or ‘Lu & Ades model’) have been implemented as well as network meta-regression models that can incorporate covariates. The command contains also an option that enables the estimation of ranking probabilities.
What is GeMTC software?
The GeMTC software can be used either via a GUI (graphical user interface) application or via R. The software automatically generates models for network meta-analysis suitable for MCMC software, like WinBUGS, OpenBUGS and JAGS. The models can be then run directly or exported. It can generate random effects consistency and inconsistency models (the ‘Lu & Ades model’) as well as ‘node-splitting models’ for checking inconsistency. The output of the consistency model includes also the estimation of ranking probabilities.
What is the purpose of the network graphs package in Stata?
These graphs can be used to present the evidence base, the assumptions and the results of a network meta-analysis and aim to make the methodology accessible also to non-statisticians. Some of the commands are used combined with the mvmeta command.
What are the websites of MPES and IMMA?
The websites of MPES program (University of Bristol) and IMMA project (University of Ioannina) provide codes that can be used to perform network meta-analysis in WinBUGS, OpenBUGS or JAGS. Consistency fixed and random effects models are available for different types of data. The two websites provide also material (such as software codes and example datasets) from published papers in the field of networks meta-analysis.
Description
Some treatments in a network meta-analysis may be combinations of other treatments or have common components. The influence of individual components can be evaluated in an additive network meta-analysis model assuming that the effect of treatment combinations is the sum of the effects of its components.
Details
Treatments in network meta-analysis (NMA) can be complex interventions. Some treatments may be combinations of others or have common components. The standard analysis provided by netmeta is a NMA where all existing (single or combined) treatments are considered as different nodes in the network.
Value
An object of classes discomb and netcomb with corresponding print, summary, and forest functions. The object is a list containing the following components:
