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.
The correlation coefficient is a common used statistic index in the management science, sociology, psychology and nursing. The meta-analysis based on data of correlation coefficients has increased nowadays. The meta package and metafor package are the two major packages in R for performing meta-analysis and can implement many types of meta-analysis, including the meta-analysis of correlation coefficients. This article gives a brief introduction of the process to perform meta-analysis of correlation coefficients using these two packages, and compares their statistical results and functions (such as plot drawing).
Meta-analysis of survival data is becoming more and more popular. The data could be extracted from the original literature, such as hazard ratio (HR) and its 95% confidence interval, the difference of actual frequency and theoretical frequency (O - E) and its standard deviation. The data can be used to calculate the combined HR using Review Manager (RevMan), Stata and R softwares. RevMan software is easy to learn, but there are some limitations. Stata and R software are powerful and flexible, and they are able to draw a variety of graphics, however, they need to be programmed to achieve.
ObjectiveTo investigate the value of smart phone Scoliometer software in obtaining optimal lumbar lordosis (LL) during L4-S1 fusion surgery.
MethodsBetween November 2014 and February 2015, 20 patients scheduled for L4-S1 fusion surgery were prospectively enrolled the study. There were 8 males and 12 females, aged 41-65 years (mean, 52.3 years). The disease duration ranged from 6 months to 6 years (mean, 3.4 years). Before operation, the pelvic incidence (PI) and Cobb angle of L4-S1 (CobbL4-S1) were measured on lateral X-ray film of lumbosacral spine by PACS system; and the ideal CobbL4-S1 was then calculated according to previously published methods [(PI+9°)×70%]. Subsequently, intraoperative CobbL4-S1 was monitored by the Scoliometer software and was defined as optimal while it was less than 5° difference compared with ideal CobbL4-S1. Finally, the CobbL4-S1 was measured by the PACS system after operation and the consistency was compared between Scoliometer software and PACS system to evaluate the accuracy of this software. In addition, value of this method in obtaining optimal LL was validated by comparing the difference between ideal CobbL4-S1 and preoperative one with that between ideal CobbL4-S1 and postoperative one.
ResultsThe CobbL4-S1 was (36.17±1.53)° for ideal one, (22.57±5.50)° for preoperative one, (32.25±1.46)° for intraoperative one measured by Scoliometer software, and (34.43±1.72)° for postoperative one, respectively. The observed intraclass correlation coefficient (ICC) was excellent [ICC=0.96, 95% confidence interval (0.93, 0.97)] and the mean absolute difference (MAD) was low (MAD=1.23) between Scoliometer software and PACS system. The deviation between ideal CobbL4-S1 and postoperative CobbL4-S1 was (2.31±0.23)°, which was significantly lower than the deviation between ideal CobbL4-S1 and preoperative CobbL4-S1 (13.60±1.85)° (t=6.065, P=0.001).
ConclusionScoliometer software can help surgeon obtain the optimal LL and deserve further dissemination.
ObjectiveTo compare the characteristics and functions of the network meta-analysis software and for providing references for users.
MethodsPubMed, CNKI, official website of Stata and R, and Google were searched to collect the software and packages that can perform network meta-analysis up to July 2014. After downloading the software, packages, and their user guides, we used the software and packages to calculate a typical example. The characteristics, functions, and computed results were compared and analyzed.
ResultsFinally, 11 types of software were included, including programming and non-programming software. They were developed mainly based on Bayesian or Frequentist. Most types of software have the characteristics of easy to operate, easy to master, exactitude calculation, or good graphing; however, there is no software that has the exactitude calculation and good graphing at the same time, which needs two or more kinds of software combined to achieve.
ConclusionWe suggest the user to choose the software at least according to personal programming basis and custom; and the user can consider to choose two or more kinds of software combined to finish the objective network meta-analysis. We also suggest to develop a kind of software which is characterized of fully function, easy operation, and free.
In systematic reviews and meta-analyses, time-to-event outcomes were mostly analysed using hazard ratios (HR). It was neglected transformation of the data so that some wrong outcomes were gained. This study introduces how to use Stata and R software to calculate the HR correctly if the report presents HR and confidence intervals were gained.
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.
In evidence-based practice and decision, dose-response meta-analysis has been concerned by many scholars. It can provide unique dose-response relationship between exposure and disease, with a high grade of evidence among observational-study based meta-analysis. Thus, it is important to clearly understand this type of meta-analysis on software implementations. Currently, there are different software for dose-response meta-analysis with various characteristics. In this paper, we will focus on how to conduct dose-response meta-analysis by Stata, R and SAS software, which including a brief introduction, the process of calculation, the graph drawing, the generalization, and some examples of the processes.
R software is a free and powerful statistical software, which has the functions of data processing and graphics drawing and is widely used in the meta-analysis. This article introduced detailed operation steps of rmeta package which is suitable for meta-analysis of binary data, and also can finish cumulative meta-analysis, draw standard summary plots, forest plot and funnel plot. In summary, the functions of rmeta package are simple and easy to mastery. There are some limitations of current version which need to be improved in the future.
ObjectiveTo investigate current status and hot issues of pancreatic neuroendocrine neoplasm (pNEN) imaging research.MethodsThe literatures focusing on pNEN and published from 1998 to 2018 were retrieved from the core database of Web of Science. The quantitative analysis of literatures was then conducted by using the CiteSpace software based on the bibliometrics method. The research trend was then summarized systematically and the potential research fronts and focuses were explored.ResultsA total of 190 articles in the field of pNEN imaging research were retrieved, and the top three countries in the literatures were the United States, Germany, and Italy. The clustering of co-citation of pNEN included the endoscopic ultrasound, current diagnosis, prospective evaluation, cystic pancreatic neuroendocrine tumor, hypervascular neuroendocrine tumor, nonfunctioning pancreatic neuroendocrine tumor, intravoxel incoherent motion, and metastastic lesion. The hot of keywords in the field of pNEN included the fine needle aspiration, CT, diagnosis, pancreas, cancer, neuroendocrine tumor, neoplasm, carcinoma, and management. The hot keywords clustering had the neuroendocrine tumor, pancreatic mass size, non-hyperfunctioning neuroendocrine tumor, CT appearance, metastatic lesion, ancillary studies, somatostatin analogues, somatostatinoma, intraoperative ultrasound, and multiple endcorine neoplasia 1.ConclusionAccurate imaging diagnosis of pNEN is still a hot issue in this field.
