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.
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.
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.
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.
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.
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.
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.
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.
ObjectiveTo combine specific examples and R Studio language code, to apply the Bayesian quantile regression method in the analysis of clinical medicine data, and show the advantages of Bayesian quantile regression method, so as to provide references for improving the accuracy of medical research. Methods The clinical data of 250 patients with knee osteoarthritis from the capital special research on the application of clinical characteristics project were used. A Bayesian quantile regression model based on data set was constructed to explore the relationship between the level of serum IgG and the age of the patients. Results The Monte Carlo algorithm converge can judge the efficiency of parameter estimation based on Gibbs sampling which was used to draw samples from the posterior distribution of parameters in Bayesian quantile regression. By generating the parameter into the regression formula, we can obtain the regression under different quantiles: Y1=−6.022 063 47+2.026 913 73X−0.015 077 69X2……Y5=24.610 542 414−0.395 059 497X+0.004 205 064X2. It can be found that the serum level of IgG was obviously increased with age. Conclusion Bayesian quantile regression parameter estimation results are accurate and highly credible, and reliable parameter information can be obtained even under small sample conditions. It has great advantages in the research of clinical medicine data and has certain promotional value.
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.