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.
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.
The key for performing meta-analysis using WinBUGS software is to construct a model of Bayesian statistics. The hand-written code model and Doodle model are two major methods for constructing it. The approach of hand-written code is flexible and convenient, but the language programming is fallibility. The Doodle is complicated, but it is benefit to understand the structure of hand-written code model and prevent error. This article briefly describes how to construct the Doodle model for binary and continuous data of head to head meta-analysis, indirect comparison and network meta-analysis, and ordinal variables meta-analysis.
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.
NetMetaXL is a macro command to conduct network meta-analysis in the frame of Microsoft Excel on basis of Bayesian theory. This macro command, which was officially launched in 2014, integrates data extraction and entry, analysis results output and graph plotting as a whole. Currently, this version contains enough optional models, and all operations are through menu and easy to conduct; however, it is appropriate only for the network meta-analysis based on dichotomous variables, which still has fairly a lot to be enhanced and improved. This article gives a brief introduction based on examples to implement network meta-analysis using NetMetaXL.
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.
The choice of genetic models was main difficulty in the meta-analysis of gene-disease association studies. In this study, we made a further discussion about the genetic model-free approach that proposed by Minelli et al. The program that coded by JAGS and R was carried out to perform the Bayesian procedure. In a real example, several kinds of prior distribution were used, including non-informative prior distribution and external clinical prior information. Especially, compared to Minelli’s study, we introduced clinical prior information. The results indicated that the pooled results were rather robust no matters the prior distribution were non-informative or informative, especially when the number of included studies were large.
ObjectivesTo systematically review the efficacy of seven types of cognitive interventions for older adults with mild to moderate Alzheimer's Disease (AD).MethodsWe searched The Cochrane Library, PubMed, EMbase, CNKI, WanFang Data, VIP and CBM databases to collect randomized controlled trials on cognitive interventions for mild to moderate Alzheimer's Disease (AD) from inception to January 2018. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies. STATA 14.0 software was then used to perform a meta-analysis.ResultsA total of 49 randomized controlled trials (RCTs) were included. The results of network meta-analysis revealed that each cognitive intervention had significantly improved the cognitive ability of AD patients. Specifically, nursing intervention (NI) (MD=3.01, 95%CI 1.70 to 4.50, P<0.005) was the most effective enhancer of cognitive ability, followed by music therapy (MT) (MD=2.60, 95%CI 0.96 to 4.30, P<0.001), physical exercise (PE) (MD=2.4, 95%CI 1.0 to 3.9, P<0.001), cognitive rehabilitation (CR) (MD=2.3, 95% CI 0.92 to 3.7, P=0.013), cognitive simulation (CS) (MD=1.7, 95%CI 1.2 to 2.3, P=0.037), computerized cognitive training (CCT) (MD=1.6, 95%CI 0.42 to 2.8, P<0.001), and pharmacological therapies (PT) (MD=1.5, 95%CI 0.24 to 2.8, P=0.041).ConclusionsThe seven types of cognitive interventions are helpful in improving the cognitive ability of Alzheimer's patients, and nursing intervention is the most effective cognitive intervention. Moreover, non-pharmacological therapies may be better than pharmacological therapies.
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.