The theoretical foundation of relevant packages of R software for network meta-analysis is mainly based on Bayesian statistical model and a few of them use generalized linear model. Network meta-analysis is performed using GeMTC R package through calling the corresponding rjags package, BRugs package, or R2WinBUGS package (namely, JAGS, OpenBUGS, and WinBUGS software, respectively). Meanwhile, GeMTC R package can generate data storage files for GeMTC software. Techonically, network meta-analysis is performed through calling the software based on Markov Chain Monte Carlo method. In this article, we briefly introduce how to use GeMTC R package to perform network meta-analysis through calling the OpenBUGS software.
Network plots can clearly present the relationships among the direct comparisons of various interventions in a network meta-analysis. Currently, there are some methods of drawing network plots. However, the information provided by a network plot and the interface-friendly degree to a user differ in the kinds of software. This article briefly introduces how to draw network plots using the network package and gemtc package that base on R Software, Stata software, and ADDIS software, and it also compares the similarities and differences among them.
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.
R Software is an open, free of use and charge statistical software which has a powerful graphic capability; however, it requires more complex codes and commands to perform network meta-analysis, which causes errors and difficulties in operation. WinBUGS software is based on Bayesian theory, which has a powerful data processing capability, and especially its codes are simple and easy to operate for dealing with network meta-analysis. However, its function of illustrating statistical results is very poor. In order to fully integrate the advantages of R software and WinBUGS software, an R2WinBUGS package based on R software has been developed which builds a “bridge” across two of them, making network meta-analysis process conveniently, quickly and result illustration more beautiful. In this article, we introduced how to use the R2WinBUGS package for performing network meta-analysis using examples.
R software is a free and powerful statistical tool, including Metafor, Meta as well as Rmeta packages, all of which could conduct meta-analysis. Metafor package provides functions for meta-analyses which include analysis of continuous and categorical data, meta-regression, cumulative meta-analysis as well as test for funnel plot asymmetry. The package can also draw various plots, such as forest plot, funnel plot, radial plot and so forth. Mixed-effects models (involving single or multiple categorical and/or continuous moderates) can only be fitted with Metafor packages. Advanced methods for testing model coefficients and confidence intervals are also implemented only in this package. This article introduces detailed operation steps of Metafor package for meta-analysis using cases.
Objective To evaluate the effectiveness and safety of Octreotide combined with Ulinastatin for treating acute pancreatitis in China. Methods The databases such as CBM, VIP, CNKI and WanFang Data were searched to collect randomized controlled trials (RCTs) from the date of their establishment to February 2011, and the relevant references of the included studies were also retrieved. Studie were screened, data were extracted, and the methodological quality was assessed by two reviewers independently. Meta-analyses were conducted by using RevMan 5.1 software. Results A total of 12 studies involving 1 023 participants were included. The results showed that compared with the group of routine therapies and the group of single administration of either Octreotide or Ulinastatin, the experimental group of Octreotide combined with Ulinastatin was superior in the following aspects with singnificant differences: the total effective rate (RR= 0.34, 95%CI 0.23 to 0.52), the remission time of abdominal pain and distention (SMD= –0.89, 95%CI –1.09 to –0.70), the remission time of signs of abdominal tenderness (SMD= –0.95, 95%CI –1.48 to –0.42), the average length of hospital stay (SMD= –1.10, 95%CI –1.58 to –0.63), the time for blood amylase returning to normal (SMD= –1.14, 95%CI –2.10 to –0.17) and the positive cases at the end of treatment (RR= 0.20, 95%CI 0.08 to 0.51), the time for urine amylase returning to normal (SMD= –0.86, 95%CI –1.04 to –0.68) and the positive cases at the end of treatment (RR= 0.27, 95%CI 0.12 to 0.63), the IL-6 level at the end of treatment (SMD= –2.25, 95%CI –4.39 to –0.11), the incidence rate of complications (RR= 0.39, 95%CI 0.28 to 0.55), the required rate of operation (RR= 0.41, 95%CI 0.24 to 0.69), and the mortality (RR= 0.43, 95%CI 0.29 to 0.64). But the experimental group showed a little longer time for blood calcium returning to normal without statistic difference (MD =0.15, 95%CI 0.05 to 0.26).Conclusion According to the domestic evidence, Octreotide combined with Ulinastatin for treating acute pancreatitis is superior to both the routine therapies and the singe administration of either Octreotide or Ulinastatin. It provides a new and prospective therapeutic method for AP. However, this conclusion has to be further verified by high quality, large scale and double blinded RCTs.
Evidence is the core of Evidence-Based Medcine; the Grades of Recommendations Assessment, Development and Evaluation (GRADE System) is a milestone in the history of evidence development. This paper outlines the GRADE System and GRADEpro 3.2 software, and briefly explores the right and wrong application which was published in the Chinese Journal of Evidence-Based Medicine. The GRADEpro 3.2 software is easy to operate, but for evaluating the reasons of upgrade and downgrade, and the importance of the parameters of outcomes, it needs to comprehensively and systematically understand the knowledge of relevant background, and to construct a solid foundation in clinical epidemiology and systematic review. In view of this paper based on the current GRADE System, there may be some discrepancy to the later content with the GRADE System constant improvement. Therefore, it is bly recommended that readers should keep constant learning and improving.
Etiological and prognostic studies always directly reported effect size with its 95% confidence interval, hence, data transformation was needed when performing meta-analysis based on these studies. Using the data of risk ratio, hazard ratio, odds ratio and 95% confidence interval as an example, this paper introduces the process of using RevMan 5.3 software to convert data and perform meta-analysis.
Objectives To evaluate the research status of hospital management in foreign countries using bibliometric analysis, in order to provide reference for domestic hospital management. Methods The Scopus and Web of Science databases were searched for hospital management related studies from inception to May 30th, 2017. The publication date, document type, country, affiliation, publication distribution, citation, and co-authorship of included studies were analyzed. Results During the past 20 years, the amount of hospital management related studies presented an increased trend, and original article was the major article type. The USA, UK, Germany, France, Japan, Australia, Austria, Italy, Spain and Canada were ranked as the top ten countries that had published the most related studies. Moreover, most of the affiliations which published the related studies were from the USA, UK and France. The results of co-authorship analysis indicated that some researchers existed close co-authorships. Conclusions Developed countries have better researches on hospital management and can provide a good reference for domestic researchers.
Health economics analysis has become increasingly important in recent years. It is essential to master the use of relevant software to conduct research in health economics. TreeAge Pro software is widely used in the healthcare decision analysis. It can carry out decision analysis, cost-effectiveness analysis, and Monte Carlo simulation. With powerful functionlity and outstanding visualization, it can build Markov disease transition models to analyze Markov processes according to disease models and accomplish decision analysis with decision trees and influence diagrams. This paper introduces cost-effectiveness analysis based on Markov model with examples and explains the main graphs.