Node-Splitting Generalized Linear Mixed Models for Evaluation of Inconsistency in Network Meta-Analysis.

BACKGROUND: Network meta-analysis for multiple treatment comparisons has been a major development in evidence synthesis methodology. The validity of a network meta-analysis, however, can be threatened by inconsistency in evidence within the network. One particular issue of inconsistency is how to directly evaluate the inconsistency between direct and indirect evidence with regard to the effects difference between two treatments. A Bayesian node-splitting model was first proposed and a similar frequentist side-splitting model has been put forward recently. Yet, assigning the inconsistency parameter to one or the other of the two treatments or splitting the parameter symmetrically between the two treatments can yield different results when multi-arm trials are involved in the evaluation. OBJECTIVES: We aimed to show that a side-splitting model can be viewed as a special case of design-by-treatment interaction model, and different parameterizations correspond to different design-by-treatment interactions. METHODS: We demonstrated how to evaluate the side-splitting model using the arm-based generalized linear mixed model, and an example data set was used to compare results from the arm-based models with those from the contrast-based models. RESULTS & CONCLUSIONS: The three parameterizations of side-splitting make slightly different assumptions: the symmetrical method assumes that both treatments in a treatment contrast contribute to inconsistency between direct and indirect evidence, whereas the other two parameterizations assume that only one of the two treatments contributes to this inconsistency. With this understanding in mind, meta-analysts can then make a choice about how to implement the side-splitting method for their analysis.

MiRE: a graphical R package for microRNA-related analysis / 中国医学科学杂志(英文版)

<p><b>OBJECTIVE</b>To provide a set of useful analysis tools for the researchers to explore the microRNA data.</p><p><b>METHODS</b>The R language was used for generating the Graphical Users Interface and implementing most functions. Some Practical Extraction and Report Language (Perl) scripts were used for parsing source files.</p><p><b>RESULTS</b>We developed a graphical R package named miRE, which was designated for the analysis of microRNA functions, genomic organization, etc. This package provided effective and convenient tools for molecular biologists to deal with routine analyses in microRNA-related research. With its help, the users would be able to build a desktop-centered microRNA research environment quite easily and effectively. miRE is freely available at http://www. biosino.org/-kanghu/WorkPresentation/miRE/miRE.html. A detailed user manual and tutorials with example code and image are also available.</p><p><b>CONCLUSION</b>miRE is a tool providing an open-source, user-friendly, integrated interface for microRNA-related analysis. With its help, researchers can perform microRNA-related analysis more efficiently.</p>

A primary study using the method of average positive stained area percentage to measure the immunohistochemistry results / 生物医学工程学杂志

With the help of computer image analysis system, we used the method of average positive stained area percentage APSAP to evaluate the slice immunohistochemistry result. Then we compared the evaluation result with the result of manual counting. Conformity between the two methods was verified. These data indicated that the method of was in accord with manual counting to a great extent. Moreover, the theory basis, advantages and disadvantages of the method were discussed in this paper.