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.
Meta-analyses include meta-analysis of the published literature (MPL) and meta-analysis of individual patient data (MIPD). Recursive cumulative meta-analysis is a method used to reorganize the secondary analysis data based on original studies thus to ensure a timely update, in addition, it can also be used to analyze the data from longer followup of existing trials. By using this method, with the each newly included or updated study, the change of pooled effect size in each pooled step can be detected, therefore, the bias/heterogeneity and stability of pooled results can be evaluated. In this article, we briefly introduced the concept of recursive cumulative meta-analysis and an example was used to illustrate this method.
Network meta-analysis (NMA) is a new statistical approach which comes from head to head meta-analysis. Hence, NMA inherits all methodology challenges of head to head meta-analysis and with increased complexity results due to more intervention treatments involved. The issue of sample size and statistical power in individual trial and head to head meta-analysis is widely emphasized currently; however, they are not been paid due attention in NMA. This article aims to introduce the theory, computational principles and software implementation using examples with step by step approach.
Meta-analysis has been regarded as the critical tool of assisting the healthcare professionals to make decisions. And the theory of evidence-based medicine is widely disseminated in domestic. However, it must be noted that the increasing number of meta-analyses causes a fact that several meta-analyses investigating the same or similar clinical questions were captured commonly. More importantly, the results from these meta-analyses are often conflicting. Consequently, decision-making of those healthcare professionals who depend on those results become a thorny thing. To address this issue, Jadad et al. from McMaster University proposed an adjunct algorithm to help healthcare professionals to select the best result from conflicting meta-analyses to make decisions properly. Our article will introduce the tool briefly and explain the process of it with an example.
Joanna Briggs Institute (JBI) is an international collaboration center for evidence-based healthcare, which mainly focuse on evidence-based activities in nursing science, rehabilitation science and psychiatry science. The present article systematically and comprehensively introduces the foundation, development, mission, organizational structure, and the major contents of the JBI institute, so as to provide theory support for Chinese researchers who specialize in studying methods and practice in these given fields.