The R software bmeta package is a package that implements Bayesian meta-analysis and meta-regression by invoking JAGS software. The program is based on the Markov Chain Monte Carlo (MCMC) algorithm to combine various effect quantities (OR, MD and IRR) of different types of data (dichotomies, continuities and counts). The package has the advantages of fewer command function parameters, rich models, powerful drawing function, easy of understanding and mastering. In this paper, an example is presented to demonstrate the complete operation flow of bmeta package to implement bayesian meta-analysis and meta-regression.
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.
ObjectiveTo systematically review the influence of health education on medicine-taking compliance of hypertensive patients, so as to provide scientific evidence for health decision-making.
MethodsLiterature search was performed in CBM, CNKI, WanFang Data and VIP databases to collect randomized controlled trials (RCTs) published between 1998 and 2013 concerning the effect of health education on medicine-taking compliance of hypertensive patients. Two reviewers independently screened the literature according to the inclusion and exclusion criteria, extracted the data, assessed the methodological quality of included studies, and then conducted Bayesian meta-analysis using WinBUGS 14 software after heterogeneity-test by using Stata 10.0 software.
ResultsA total of 19 RCTs involving 3 751 participants were included. The results of Bayesian meta-analysis showed that the health education group was superior to the control group in medicine-taking compliance with a significant difference (OR=4.46, 95%CI 3.698 to 5.358).
ConclusionHealth education could enhance the medicine-taking compliance of Chinese hypertension patients significantly.
Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: ① compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.
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 to 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.
This study introduced the construction of individualized risk assessment model based on Bayesian networks, comparing with traditional regression-based logistic models using practical examples. It evaluates the model's performance and demonstrates its implementation in the R software, serving as a valuable reference for researchers seeking to understand and utilize Bayesian network models.
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.
ObjectiveA simulation study was used to generate the multivariate normal distribution data with a residual effect based on series of N-of-1 trials. The statistical performance of paired t-test, mixed effect model and Bayesian mixed effect model were compared.MethodsThree-cycles N-of-1 trials were set, and the participants were randomly assigned to 2 different treatments in each cycle. The simulation study included the following procedures: producing six-dimensional normal distribution data, randomly allocating intervention methods and patients, adding residual effects, constructing and evaluating 3 models, and setting the parameters. The sample sizes were set as 3, 5, 8 and 10, and the correlation coefficients among different times were set as 0.0, 0.5 and 0.8. Different proportions of residual effects for the 2 groups were set. Type I error, power, mean error (ME), and mean square error (MSE) were used to compare the 3 models.ResultsWhen there was no residual effect in the 2 groups, type I errors of 3 models were approximately 0.05, and their MEs were approximately 0. Paired t-test had the highest power and the lowest MSE. When the residual effect existed in the 2 groups, the type I error of paired t-test increased, and its estimated value deviated from the true value (ME≠0). Type I errors of the mixed effect model and Bayesian mixed-effect model were approximately 0.05, and they had the same power. The estimated values of the two models were close to the true value (ME was approximately 0).ConclusionsWhen there is no residual effect (0% vs. 0%), paired t-test is suitable for data analysis of N-of-1 trials. When there is a residual effect, the mixed effect model and Bayesian mixed-effect model are suitable for data analysis of N-of-1 trials.
In this paper, the research has been conducted by the Microsoft kinect for windows v2 for obtaining the walking trajectory data from hemiplegic patients, based on which we achieved automatic identification of the hemiplegic gait and sorted the significance of identified features. First of all, the experimental group and two control groups were set up in the study. The three groups of subjects respectively completed the prescribed standard movements according to the requirements. The walking track data of the subjects were obtained straightaway by Kinect, from which the gait identification features were extracted: the moving range of pace, stride and center of mass (up and down/left and right). Then, the bayesian classification algorithm was utilized to classify the sample set of these features so as to automatically recognize the hemiplegia gait. Finally, the random forest algorithm was used to identify the significance of each feature, providing references for the diagnose of disease by ranking the importance of each feature. This thesis states that the accuracy of classification approach based on bayesian algorithm reaches 96%; the sequence of significance based on the random forest algorithm is step speed, stride, left-right moving distance of the center of mass, and up-down moving distance of the center of mass. The combination of step speed and stride, and the combination of step speed and center of mass moving distance are important reference for analyzing and diagnosing of the hemiplegia gait. The results may provide creative mind and new references for the intelligent diagnosis of hemiplegia gait.
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.
