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.
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.