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.
ObjectiveTo systematically review the correlation between apolipoprotein E (ApoE) polymorphism and sporadic Alzheimer's disease (SAD) in Chinese population. MethodsThe case-control studies about the relationship between ApoE polymorphism and SAD in Chinese population were electronically retrieved in PubMed, EMbase, CBM, The Cochrane Library (Issue 8, 2013), CNKI, VIP, and WanFang Data from the date of their establishment to August 2013. Literature screening according to the inclusion and exclusion criteria, data extraction and methodological quality assessment of the included stuides were completed by two reviewers independently. Meta-analysis was then conducted using Stata 12.0 software. ResultsA total of 50 case-control studies invovling 3 396 cases and 4 917 controls were finally included. The results of meta-analysis showed that, in Chinese, the risk of SAD was 2.89 times higher in population with allele ε4 than in population with allele ε3 (OR=2.89, 95%CI 2.61 to 3.19, P < 0.001); 7.24 times higher in those with ε4/ε4 genotype than in those with ε3/ε3 genotype (OR=7.24, 95%CI 5.11 to 10.24, P < 0.001); 2.90 times higher in ε3/ε4 genotype than in ε3/ε3 genotype (OR=2.90, 95%CI 2.56 to 3.29, P < 0.001); 2.11 times higher in ε2/ε4 genotype than in ε3/ε3 genotype (OR=2.11, 95%CI 1.64 to 2.72, P < 0.001); and no statistic significance was found in the risk of SAD compared ε2/ε3, ε2/ε2 genotypes and ε2 allele with ε3/ε3 genotype and ε3 allele. ConclusionFor Chinese population, ApoE allele ε4 is significantly associated with the onset of SAD, and genotype ε4/ε4 is a high risk factor of SAD. While allele ε2 is not associated with the onset of SAD. Since a great deal of current studies failed to conduct stratified analysis, it is suggested to further conduct relevant relevant studies according to clinical classification of SAD and patients' characteristics.
The mada package is a type of package that is especially used for implementing meta-analysis of diagnostic accuracy tests. This package is developed on basis of classical statistical theories and it can be used to calculate all relevant effect size of diagnostic accuracy tests; however, it does not provide pooled values of sensitivity and specificity. This article uses an example to introduce the whole functions of mada package in implementing meta-analysis of diagnostic accuracy tests, including data preparation, calculation implementation, result summary, and plots drawing.
Compared with traditional head to head meta-analysis, network meta-analysis has more confounding factors and difficulties to handle. Due to the mutual transitivity of evidence in network meta-analysis, heterogeneity may be brought into indirect meta-analysis. Hence, effective differentiation and correct handling of heterogeneity are being current focus. In order to ensure the reliability of the results of network meta-analysis, the concept of homogeneity is proposed and a series of methods are developed for differentiation and handling of homogeneity. Based on the extension of Bucher methods, current methods for differentiation and handling of homogeneity has extended to ten quantitative measures (eg., node analysis method, hypothesis tests, and two-step method). However, because of the differences and the focus of fundamental methodological theories as well as the limitation of statistics power, no highly-effective method has been worked out. Therefore, the exploration of highly-effective, simple and high-resolved methods are still needed.
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.