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 examine statistical performance of different rare-event meta-analyses methods.MethodsUsing Monte-Carlo simulation, we set a variety of scenarios to evaluate the performance of various rare-event meta-analysis methods. The performance measures included absolute percentage error, root mean square error and interval coverage.ResultsAcross different scenarios, the absolute percentage error and root mean square error were similar for Bayesian logistic regression model, generalized mixed linear effects model and continuity correction, but the interval coverage was higher with Bayesian logistic regression model. The statistical performances with Mantel-Haenszel method and Peto method were consistently suboptimal across different scenarios.ConclusionsBayesian logistic regression model may be recommended as a preferred approach for rare-event meta-analysis.
Objective To analyze the spatiotemporal trends in hepatitis B-related mortality and disability-adjusted life years (DALYs) attributable to high BMI at the global, regional, and national levels. Methods We extracted data on hepatitis B-related mortality numbers, DALYs, age-standardised mortality rates (ASMR), and age-standardised DALY rates (ASDR) attributed to high BMI from the GBD 2021 database for the period 1990-2021, stratified by gender, age, country, and social demographic index (SDI). Time trends were assessed using estimated annual percentage change (EAPC), and decomposition analysis and frontier analysis were employed to identify the drivers of burden changes and leading countries. Inequality indicators (inequality slope index SII and concentration index CI) were used to measure health disparities across SDI levels, and the Bayesian Age Period Cohort Model (BAPC) was applied to predict disease trends up to 2050. Results The global burden of hepatitis B disease attributable to high BMI continues to rise. In 2021, the number of DALYs reached 499 900 (four times that of 1990), and the number of deaths was five times that of 1990. The burden and rate of increase were most pronounced in Asia: in 2021, East Asia recorded 7 919.70 deaths (95%UI 2 984.05 to 14 386.39) and 257 954.31 DALYs (95%UI 97 807.17 to 482 232.54), ranked highest among the 21 GBD regions; From 1990 to 2021, South Asia recorded the fastest increase in ASMR (EAPC=4.99, 95%CI 4.83 to 5.16) and the highest growth rate in ASDR (EAPC=4.92, 95%CI 4.74 to 5.10); at the national level, China and the United States had the heaviest burden. Countries with medium SDI had the highest burden, peaking at an SDI of 0.65. Global and regional decomposition analyses indicate that epidemiological changes are the primary drivers of the increased burden. The CI and SII values derived from inequality analyses of ASDR and ASMR have both increased, indicating worsening health inequalities. Frontier analysis further confirmed that certain countries, such as Tonga and Mongolia, bear a significantly higher burden than expected for their developmental level, demonstrating marked disparities in disease burden across nations. The BAPC model predicts that the burden attributable to high BMI will continue to rise in the absence of interventions. Conclusion High BMI has become an important risk factor for hepatitis B-related diseases globally, with the burden particularly pronounced in Asian regions and middle-income countries. Health inequalities must not be overlooked. Precise interventions should be implemented based on regional, gender, and age differences.
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.
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.
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.
Objective To analyze the substitution mechanism of surrogate endpoints for traditional Chinese medicine (TCM) clinical efficacy evaluation of chronic heart failure (CHF). Methods To obtain data from the occurrence of surrogate endpoints and cardiogenic death of patients with CHF in 7 hospitals. The causal relationship between surrogate endpoints and cardiogenic mortality was inferred by the Bayesian network model, and the interaction among surrogate endpoints was analyzed by non-conditional logistic regression model. Results A total of 2 961 patients with CHF were included. The results of Bayesian network causal inference showed that cardiogenic mortality had a causal relationship with the surrogate endpoints including NYHA classification (P=0.46), amino-terminal pro-B-type natriuretic peptide (NT-proBNP) (P=0.24), left ventricular ejaculation fraction (LVEF) (P=0.19), and hemoglobin (HB) (P=0.11); non-conditional logistic regression analysis showed that NYHA classification had interaction with NT-proBNP, LVEF, and HB prior to and after adjusting confounders. Conclusions The substitution capability of surrogate endpoints for TCM clinical efficacy evaluation of CHF for cardiogenic mortality are NYHA classification, NT-proBNP, LVEF, and HB in turn, and there is a multiplicative interaction between the main surrogate endpoint NYHA classification and the secondary surrogate endpoints including NT-proBNP, LVEF, and HB, suggesting that when the two surrogate endpoints with interaction exist at the same time, it can enhance the substitution capability of surrogate endpoints for cardiogenic mortality.
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.
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.
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.
