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.
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.
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.
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.
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.
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.
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.
The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.
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.