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.
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.
