Network meta-analysis (NMA) is a statistical technique that integrates data from multiple clinical studies and compares the efficacy and safety of multiple interventions, which can provide pro and con ranking results for all intervention options in the evidence network and provide direct evidence support for clinical decision-making. At present, NMA is usually based on the aggregation of the same type of data set, and there are still methodological and software difficulties in achieving cross-study design and cross-data format data set merging. The crossnma package of R programming language is based on Bayesian framework and Markov chain Monte Carlo algorithm, extending the three-level hierarchical model to the standard NMA data model to achieve differential merging of varied data types. The crossnma package fully considers the impact of risk bias caused by the combination of different types of data on the results by introducing model variables. In addition, the package provides functions such as result output and easy graphing, which makes it possible to combine NMA across study designs and evidence across data formats. In this study, the model based on crossnma package method and software operation will be demonstrated and explained through the examples of four individual participant datasets and two aggregate datasets.
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.