The Validity of the Assessment Criteria and Data Extraction in Meta-Analysis Articles
10.11256/jjdi.18.22
- VernacularTitle:メタアナリシス論文における評価基準の妥当性とデータ抽出
- Author:
Hisashi Iijima
;
Tomoka Osumi
- Publication Type:Journal Article
- Keywords:
meta-analysis;
ITT;
sensitivity analysis;
incretin;
DPP4 inhibitor
- From:Japanese Journal of Drug Informatics
2016;18(1):22-32
- CountryJapan
- Language:English
-
Abstract:
Objective: Clinical articles are important for individualization of drug therapy. Especially, meta-analysis is positioned at the highest evidence level. Therefore, we assessed the Quality Score of Meta-Analysis (QSMA) which provides simple assessments of both the quality and the format of meta-analyses, by applying them to incretin-related drugs as a model. Furthermore, we attempted to extract clinical data from the literature employing a certain minimum standard.
Method: We searched for meta-analyses of incretin-related drugs for diabetes in PubMed and the Cochrane Library, scoring the extracted articles for format using PRISMA statements, and for quality using QSMA. Additionally, we classified these articles into two groups with a QSMA score of 70% as the basis, and verified the analysis sets (ITT, FAS, PP or APT) and sensitivity analysis. Furthermore, we looked into those articles that scored 70% or higher to extract data that were deemed to have significant statistical differences.
Results: Scoring of the 66 articles studied yielded 69.9±19.4% (mean ± SD) for format and 62.1±17.8% for quality. These two variables produced a regression line of y=0.777x+7.834. Comparison of the two groups classified on the basis of a 70% score on QSMA yielded a significant difference in sensitivity analysis only (p<0.05). Seven effects and five side effects were extracted from articles with a QSMA score of 70% or higher.
Conclusion: Although QSMA can provide simple assessment of quality and structure using eight items, analysis sets needs to be verified individually. As the articles assessed provided statistically endorsed data, the clinical application of QSMA will be an issue in the future.
