The "bnma" package is a Bayesian network meta-analysis software package developed based on the R programming language. The network meta-analysis was performed utilizing JAGS software, which yielded relevant results and visual graphs. Moreover, this software package provides support for various data structures and types, while also providing the advantages of flexible utilization, user-friendly operation, and deliver of rich and accurate outcomes. In this paper, using a network meta-analysis example of different therapies for androgenetic alopecia, the operational process of conducting network meta-analysis using the "bnma" package is briefly introduced.
The netmeta package is specialized for implementing network meta-analysis. This package was developed based on the theories of classical frequentist under R language framework. The netmeta package overcomes some difficulties of the software and/or packages based on the theories of Bayesian, for these software and/or packages need to set prior value when conducting network meta-analysis. The netmeta package also has the advantages of simple operation process and ease to operate. Moreover, this package can calculate and present the individual matched and pooled results based on the random and fixed effect model at the same time. It also can draw forest plots. This article gives a briefly introduction to show the process to conduct network meta-analysis using netmeta package.
Bayesian N-of-1 trials is increasingly popular in recent years. This study introduced the principle, statistical requirements, application status, advantages and disadvantages of Bayesian N-of-1 trials. Although the application of Bayesian N-of-1 trials is still limited in small scale and some problems remain to be solved, but it can provide more posterior information, and it can be the most important type of N-of 1 trial in future.
BUGSnet is a powerful R project package for Bayesian network meta-analysis. The package is based on JAGS and enables high-quality Bayesian network meta-analysis according to recognized reporting guidelines (PRISMA, ISPOR-AMPC-NCA and NICE-DSU). In this paper, we introduced the procedure of the BUGSnet package for Bayesian network meta-analysis through an example of network meta-analysis of steroid adjuvant treatment of pemphigus with continuous or dichotomous data.
The method of evaluating clinical efficacy of traditional Chinese medicine is one of the hotspots in the field of traditional Chinese medicine in recent years. How to dynamically evaluate individual efficacy is one of the key scientific problems to explain the clinical efficacy of traditional Chinese medicine. At present, there are no recognized methods of evaluating individual efficacy of traditional Chinese medicine. In this study, we provided a method of dynamically evaluating individual efficacy of traditional Chinese medicine based on Bayesian N-of-1 trials after analyzing the current status of researches on methods of evaluating individual efficacy of traditional Chinese medicine. This method has the advantages of both N-of-1 trials and Bayesian multilevel models. It is feasible to evaluate individual efficacy of traditional Chinese medicine from the perspective of the design and analysis method. This study can provide an important basis for enriching and improving the methodology of evaluating individual efficacy of traditional Chinese medicine.
ObjectiveTo introduce a Bayesian meta-analysis method for quantitatively integrating evidence from both randomized controlled trials (RCTs) and non-randomized studies of interventions (NRSIs), using concrete examples and R code, thereby supporting the combined utilization of both study types in empirical research. MethodsUsing a meta-analysis on the association between low-dose methotrexate exposure and melanoma as an example, we employed the jarbes package in R to conduct both a traditional Bayesian meta-analysis and a Bayesian nonparametric bias-correction meta-analysis model for quantitative integration. The differences between the two pooled results were then compared. ResultsThe traditional Bayesian meta-analysis indicated a posterior probability of 99% that low-dose methotrexate exposure increases melanoma risk. The Bayesian nonparametric bias-correction meta-analysis model showed a posterior probability of 92% that low-dose methotrexate exposure increases melanoma risk. ConclusionCompared with the traditional Bayesian meta-analysis model, the nonparametric bias-correction meta-analysis model is more suitable for quantitatively integrating evidence from RCTs and NRSIs, demonstrating potential for broader application. However, the comparability between the two evidence bodies should be carefully assessed prior to quantitative integration.
The WinBUGS software can be called from either R (provided R2WinBUGS as an R package) or Stata software for network meta-analysis. Unlike R, Stata software needs to create relevant ADO scripts at first which simplify operation process greatly. Similar with R, Stata software also needs to load another package when drawing network plots. This article briefly introduces how to implement network meta-analysis using Stata software by calling WinBUGS software.
Systematic reviews and meta-analyses have become the cornerstone methodologies for integrating multi-source research data and enhancing the quality of evidence. Traditional meta-analyses often demonstrate limitations when handling multiple treatment options. Network meta-analysis (NMA) overcomes these limitations by constructing a network of evidence that encompasses various treatment options, allowing for the simultaneous comparison of both direct and indirect evidence across multiple treatment plans. This provides more comprehensive and precise support for clinical decision-making. This article comprehensively reviews the statistical principles of NMA, its three fundamental assumptions, and the statistical inference framework. It also critically analyzes the mainstream NMA software and packages currently available, such as R (including gemtc, netmeta, rjags, pcnetmeta), Stata (mvmeta, network), WinBUGS, SAS, ADDIS, and various online applications, highlighting their strengths, weaknesses, and suitable scenarios. This analysis provides researchers with a scientific and unified framework for conducting clinical studies and policy-making.
ObjectiveTo introduce Bayesian meta-analysis of dichotomous data using PROC MCMC in SAS software.MethodsA previous published systematic review was used as an example, Bayesian meta-analysis of dichotomous data was implemented by PROC MCMC in SAS software, and programming code was provided.ResultsThe log-transformed value of odds ratio (OR) was used as the efficacy. The results of the Bayesian meta-analysis were very similar to those obtained by the frequency method.ConclusionsBased on the powerful programming capabilities of SAS, PROC MCMC can easily implement Bayesian meta-analysis of dichotomous data. With the rapid development of Bayesian statistical theory, Bayesian meta-analysis will play an important role in the field of meta-analysis.
Statistical analysis of clinical trials has traditionally relied on frequentist methods, but Bayesian statistics has attracted considerable attention from regulators and researchers in recent years due to its unique advantages, and its use in clinical trials is increasing. Despite the obvious advantages of Bayesian statistics, the complexity of its design, implementation and analysis poses a number of challenges to its practical application, which may lead to an increased risk of unregulated use. This study aims to comprehensively sort out the application scenarios, common methods, special considerations and key elements of reporting of Bayesian statistical methods in clinical trials, with the aim of providing researchers with references for conducting Bayesian clinical trials, and promoting the scientific and rational application of Bayesian statistical methods in clinical trials.
