(4 of 4) Network Meta-Analysis Series: Model Implementation

in

Model Implementation with Network Meta-Analysis:

We will fit our model using WinBUGS and SAS version 9.4.  For the Bayesian implementation we will employ the binomial likelihood for dichotomous outcomes and will use uninformative prior distributions for the treatment effects, and a minimally informative prior distribution for the common heterogeneity SD depending on the outcome. Also, we will assume uninformative priors for all meta-regression coefficients. We will check for convergence using appropriate MCMC diagnostics.

GRADE quality assessment of all comparisons in the network:

Read more

(3 of 4) Network Meta-Analysis Series: Assessing Data

in

Risk of bias assessment

We will use the tool described in the Cochrane Collaboration Handbook to assess risk of bias in the included studies.  The assessment will be performed by two independent reviewers and any disagreement will resolved by consensus. We will evaluate the risk of bias in the following domains: generation of allocation sequence, allocation concealment, blinding of study personnel and participants, blinding of outcome assessor, attrition, selective outcome reporting and other domains, including sponsorship bias. Where inadequate or insufficient details of allocation concealment and other characteristics of trials are provided, we may contact the trial authors to obtain further information. Read more

(2 of 4) Network Meta-Analysis: Statistical Synthesis of Study Data

in

Characteristics of included studies:

We will generate descriptive statistics for the trial, and study population characteristics across all eligible trials, describing the types of comparisons and some important variables, either clinical or methodological (such as year of publication, age, severity of illness, sponsorship and clinical setting).

We will present the evidence in the network diagram using graphical tools that allows for appropriate visual representation of the included studies. To understand which are the most influential comparisons in the network and how direct and indirect evidence influences the final summary data, we will use the contribution matrix that describes the percentage contribution of each direct meta-analysis to the entire body of evidence. Read more

(1 of 4) Network Meta-Analysis Series: Relative Safety and Efficacy of New Marketing Drugs

in

Background

Network meta-analysis provides a global estimate of effectiveness of intervention regimes, by establishing a network between regimes combining both direct and indirect evidence from trial studies.

Meta-analyses of randomized controlled trials are considered the top of the hierarchy of clinical evidence.  However, oftentimes, head-to-head comparisons are not available or are insufficient to answer a specific clinical question. NMA overcomes this limitation by providing a global estimate of efficacy or safety of multiple intervention regimes that have limited or no direct comparisons. Furthermore, NMA allows for ranking of the intervention regimes to allow for identification of the best option amongst all available options, provided that the statistical inference is valid. This appeals to clinicians and other decision makers as NMA can be used to answer the important question of “Which treatment is the best or worst?” Read more

What is Network Meta-Analysis (NMA)?

in ,

These comparisons create a web-like analysis called a Network Diagram or Network Comparison. Other names of NMA include Multiple Treatment Meta-Analysis, Mixed Treatments Comparison, Indirect Treatment Comparison, Pair-Wise Meta-Analysis and so forth. NMA has been widely used in technology appraisals for various clinical indications by technology assessment agencies around the world. For instance, in January 2019, the FDA performed a fixed-effect network meta-analysis to investigate risk of MACE associated with Romosozumab treatment. Read more