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.
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 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.
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 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.
The risk prediction model (RPM) can be used to predict the risks of disease for individuals, playing an extremely important role in decision-making regarding disease prevention, treatment, and prognostic management. Most of the existing RPMs only utilize a single-time cross-section of variable data, so-called static models, which fail to consider the many changes during disease progression and lead to limited prediction accuracy. Dynamic prediction models can incorporate longitudinal data such as repeated measurements of variables during follow-up to capture the longitudinal changes in individual characteristics over time, describe the dynamic trajectory of individual disease risk and improve the prediction accuracy of the models; however, their application in medical research is still relatively small. In this paper, we conducted a systematic literature search to summarize the commonly used dynamic models: joint model, landmark model, and Bayesian dynamic model. By introducing their application scenarios, advantages and disadvantages, and software implementations and conducting comparisons, we aimed to provide methodological references for the future application of dynamic prediction models in medical research.
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 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.
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.