title: Glycaemic control efficacy of oral antidiabetic drugs in treating type 2 diabetes: a protocol for network meta-analysis.
abstract: BACKGROUND
  Past studies of network meta-analysis focused on evaluating drug combinations in treating type 2 diabetes but not on evaluating antidiabetic drugs in monotherapy. Clinical guidelines (eg, NICE (National Institute of Health and Care Excellence) clinical guidelines 66 and 87) were based only on the findings of individual clinical trials and pairwise meta-analysis in evaluating monotherapy. This study aims to fill this gap of research by conducting a Bayesian network meta-analysis to compare major antidiabetic drugs, including metformin, glimepiride, glyburide, glipizide, repaglinide, nateglinide, sitagliptin, vildagliptin, saxagliptin and SGLT-2 (sodium-glucose transporter-2) inhibitors.
  
  
  METHODS
  Randomised controlled trials (RCTs) on the drug therapy of type 2 diabetes with outcome measures including glycosylated haemoglobin or fasting blood glucose will be included. The quality of included RTCs will be evaluated according to the Cochrane Collaboration's risk of bias tool. Traditional pairwise meta-analysis and Bayesian network meta-analysis will be conducted to compare the efficacies of antidiabetic drugs. Sensitivity analysis on the sample size of RCTs, meta-regression analysis on the follow-up periods, dosages and baselines of outcome measure, contradiction analysis between pairwise and network meta-analyses, and publication bias analysis, will be performed.
  
  
  BACKGROUND
  Ethical approval is not required because this study includes no confidential personal data and interventions on the patients. Pairwise and network meta-analyses are based on the published RCT reports of eligible drugs in treating type 2 diabetes. The results of this study will be disseminated by a peer-reviewed publication.
  PROSPERO CRD42014010567.
authors.affiliation: State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China.
authors.forename: Yongliang
authors.initials: Y
authors.lastname: Jia
authors.affiliation: State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China.
authors.forename: Yaoguang
authors.initials: Y
authors.lastname: Lao
authors.affiliation: State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China School of Informatics, University of Edinburgh, Edinburgh, UK.
authors.forename: Siu-Wai
authors.initials: SW
authors.lastname: Leung
mesh_terms: D001499:Bayes Theorem; D001786:Blood Glucose / Q000378:metabolism*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000188:drug therapy*; D006442:Glycated Hemoglobin / Q000378:metabolism*; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use*; D017063:Outcome Assessment, Health Care / Q000379:methods; D012107:Research Design; D015201:Meta-Analysis as Topic
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents