SAS Software is a powerful and internationally-recognized programming statistical software, which can implement all kinds of meta-analysis, including network meta-analysis. Bayesian statistics is an important statistical method, which uses MCMC (Markov Chain Monte Carlo) arithmetic to conduct various statistical inference. With this idea, we implement network meta-analysis thorough PROC MCMC process and introduce this process in this article based on an example.
An integration of medical data management system based on WEB and data mining tool is reportedly in this paper. In the application process of this system, web-based medical data mining user sends requests to the server by using client browser with http protocol, the commands are then received by the server and the server calls the data mining tools remote object for data processing, and the results are sent back to the customer browser through the http protocol and presented to the user. In order to prove the feasibility of the proposed solution, the test is done under the NET platform by using SAS and SPSS, and the detail steps are given. By the practical test, it was proved that the web-based data mining tool integration solutions proposed in this paper would have its broad prospects for development, which would open up a new route to the development of medical data mining.
Longitudinal data had intrinsic correlation problems at different time points, and traditional meta-analysis techniques cannot resolve this problem. Regression coefficients based on multi-level models can fully consider the correlations of longitudinal data at various time points. This paper uses SAS software to perform multi-level regression coefficient model meta-analysis and provides programming code which is simple and easy to operate.
ObjectiveTo introduce Bayesian meta-analysis of dichotomous data using PROC MCMC in SAS software.MethodsA previous published systematic review was used as an example, Bayesian meta-analysis of dichotomous data was implemented by PROC MCMC in SAS software, and programming code was provided.ResultsThe log-transformed value of odds ratio (OR) was used as the efficacy. The results of the Bayesian meta-analysis were very similar to those obtained by the frequency method.ConclusionsBased on the powerful programming capabilities of SAS, PROC MCMC can easily implement Bayesian meta-analysis of dichotomous data. With the rapid development of Bayesian statistical theory, Bayesian meta-analysis will play an important role in the field of meta-analysis.
The SAS is considered as internationally-known standard software in the field of data processing and statistics, which is also excellent in conducting meta-analysis; however, it require users to have higher technical expertise due to its complex and difficult program coding. Assessing statistical power calculation of significance tests is one of important steps in meta-analysis. Guy Cafri et al., developed a macro (%metapower) for well implement this calculation in SAS. This macro is specifically designed to implement the statistical power calculation of overall results of meta-analysis, heterogenity, and subgroup analysis, which is easy to operate. This article introduces%metapower based on examples.
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.
Despite the rapid development of meta-analysis technology, there were currently no consolidation technology for longitudinal data. The meta-analysis model based on the generalized linear mixed-effects model can fully encapsulate the correlation between various time points and accurately estimate the final combined effect, which is an ideal model for longitudinal-data meta-analysis. Through example data, this paper used SAS software to realize longitudinal-data meta-analysis and provided programming codes.
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.
目的：研究影響機械通氣患者BIS值的相關因素。方法：42例機械通氣患者，BIS模塊連續監測鎮靜深度48小時，記錄患者鎮靜前后、每隔16小時的呼吸循環指標，BIS值、SAS評分，建立混合線形統計模型，評價BIS監測的影響因素。結果：患者的血壓、心率、呼吸頻率、PaO2、SPO2 、FiO2等均對BIS值無影響，僅有PaCO2和SAS評分與BIS相關(P＜0.05)。結論：BIS模塊監測機械通氣患者鎮靜深度，BIS值的變化與患者PaCO2及SAS評分有關。
目的：探討腦電雙頻指數(BIS)監測在機械通氣患者鎮靜深度評價中的價值。方法：選取15例機械通氣患者，靜脈注射咪唑安定達到SAS評分3～4分，持續或間斷給藥維持鎮靜深度，記錄患者每2小時的SAS鎮靜分級評分及BIS，觀察24小時。比較SAS評分與BIS值的相關性。計算BIS的敏感度和特異度，根據ROC曲線和BIS評價鎮靜深度的敏感度和特異度，尋找最適BIS值。結果:隨鎮靜深度的加深，BIS明顯降低，BIS與SAS評分呈正相關(r=0.662，P＜0.05)；SAS評分3～4分(鎮靜適度)時對應的BIS臨界值為69.5～79。結論：BIS監測與SAS評分之間具有良好的相關性，能同步客觀地監測機械通氣患者的鎮靜深度，具有一定的臨床診斷價值。