Mini health technology assessment (mini-HTA) is a decision support tool based on the principle and method of evidence-based medicine and traditional HTAs. It systematically reviews healthy technologies and provides references for decision makers at the hospital level in terms of introducing relevant health technologies. In order to provide a new insight to the decision makers, this paper briefly introduces the features of mini-HTA.
Does-response meta-analysis, which has being developed for more than 30 years, is a type of regression function and can be both linear and non-linear model. It plays an important role in investigating the relationship between dependent and independent variable. With its special advantages, dose-response meta-analysis has been widely used in evidence-based practice and decision. Currently there are several models can be used to perform dose-response metaanalysis with various advantages and disadvantages. It is vital to choose best model to perform dose-response metaanalysis in evidence-based practice. In this paper, we briefly introduce and summarize the methodology of dose-response meta-analysis.
ObjectiveTo analyze the status of systematic reviews/meta-analyses on tuberculosis. MethodsThe Web of Science was searched for systematic reviews/meta-analyses on tuberculosis up to February 4th, 2015. According to the inclusion and exclusion criteria, two reviewers screened literature and extracted data. Then SPSS 11.0 software was used to analysis data including publication year, country, institution, journal and citation situation. ResultsA total of 461 systematic reviews/meta-analyses were included. The publication numbers was increasing from 1 in 1997 to 82 in 2014. Among them, China ranked the top country (113 studies), followed by the USA and Canada. The area with the most number of countries where studies were published was Europe, followed by Asia and Africa. In terms of institution, McGill University in Canada ranked the top, followed by University of London in England and Sichuan University in China. As for the number of papers in journals, the International Journal of Tuberculosis and Lung Disease ranked the top, followed by PLOS One and European Respiratory Journal. In the terms of citation, the citation ranged from 0 to 591, and the median citation frequency was 8. ConclusionThe systematic reviews/meta-analyses on tuberculosis is gradually increasing; the developed countries are still important output areas; and China is playing more and more important role in this research field.
Dose-response meta-analysis, an important tool in investigating the relationship between a certain exposure and risk of disease, has been increasingly applied. Traditionally, the dose-response meta-analysis was only modelled as linearity. However, since the proposal of more powerful function models, which contains both linear, quadratic, cubic or more higher order term within the regression model, the non-linearity model of dose-response relationship is also available. The packages suit for R are available now. In this article, we introduced how to conduct a dose-response meta-analysis using dosresmeta and mvmeta packages in R.
The pcnetmeta package in R is a special package for performing network meta-analysis based on Bayesian theory, which combines the strength computing function of JAGS software and the special data integration and powerful graph drawing function of R software. This package conducts calculation by calling JAGS, provides 3 different models for users, and each model can yield results of 3 effect-sizes (RR, OR and RD). At the same time, this package can draw many kinds of plots, which greatly meets actual needs of users to deal with complicated network meta-analysis. In this article, we introduce how to use pcnetmeta package to perform network meta-analysis based on an example.
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).
Dose-response relationship model has been widely used in epidemiology studies, as well as in evidence-based medicine area. In dose-response meta-analysis, the results are highly depended on the raw data. However, many primary studies did not provide sufficient data and led the difficulties in data analysis. The efficiency and response rate of collecting the raw data from original authors were always low, thus, evaluating and transforming the missing data is very important. In this paper, we summarized several types of missing data, and introduced how to estimate the missing data and transform the effect measure using the existed information.
The metacor, which is developed based on the classical frequentist theory, is a specified package for performing meta-analysis of correlation coefficients in R software. This package was officially launched in 2011. Based on the DerSimonian-Laird method and Olkin-Pratt method, correlation coefficients can be directly pooled by using this package. The metacor package also can be used to draw the forest plot and is easy to use; however, it still needs to be improved. This paper briefly introduced how to perform a meta-analysis of correlation coefficients using the metacor package in R software through an example.
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.
Cumulative meta-analysis could help researchers to justify the effectiveness of the intervention and whether the obtained evidence is sufficient. However, the process of the meta-analysis does not adjust the repeated testing of the null hypothesis and neither quantifies the statistical power. The sequential meta-analysis has solved the aforementioned problems and has been widely used in the clinical practice and decision-making. Currently several methods of sequential meta-analysis have been proposed and these methods differ from each other. Of which, the methodology of trial sequential (TSA) is well developed and corresponding performance is relatively easy; the methodology of double-triangular test of Whitehead is lagged than TSA and its performance is relatively difficult; the approach of semi-Bayes refers to the theory of Bayes and it's very difficult to generalize. Our paper aimed to give a brief introduction of the methodology of the sequential meta-analysis.