Interpreting Indirect Treatment Comparisons and Network Meta-Analysis for Health-Care Decision Making: Report of the ISPOR Task Force on Indirect Treatment Comparisons Good Research Practices: Part 1
ABSTRACT Evidence-based health-care decision making requires comparisons of all relevant competing interventions. In the absence of randomized, controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best choice(s) of treatment. Mixed treatment comparisons, a special case of network meta-analysis, combine direct and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than a traditional meta-analysis. This report from the ISPOR Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on the interpretation of indirect treatment comparisons and network meta-analysis to assist policymakers and health-care professionals in using its findings for decision making. We start with an overview of how networks of randomized, controlled trials allow multiple treatment comparisons of competing interventions. Next, an introduction to the synthesis of the available evidence with a focus on terminology, assumptions, validity, and statistical methods is provided, followed by advice on critically reviewing and interpreting an indirect treatment comparison or network meta-analysis to inform decision making. We finish with a discussion of what to do if there are no direct or indirect treatment comparisons of randomized, controlled trials possible and a health-care decision still needs to be made.
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ABSTRACT: There is argument over the benefits and risks of drugs for treating chronic musculoskeletal pain. This study compared the efficacy, safety, and tolerability of diclofenac, ibuprofen, naproxen, celecoxib, and etoricoxib for patients with pain caused by osteoarthritis (OA) or rheumatoid arthritis (RA). A systematic literature review used Medline and EMBASE to identify randomized controlled trials. Efficacy outcomes assessed included: pain relief measured by visual analogue scale (VAS); Western Ontario McMaster Universities Arthritis Index (WOMAC) VAS or WOMAC Likert scale; physical functioning measured by WOMAC VAS or Likert scale; and patient global assessment (PGA) of disease severity measured on VAS or 5-point Likert scale. Safety outcomes included: Antiplatelet Trialists' Collaboration (APTC), major cardiovascular (CV) and major upper gastro-intestinal (GI) events, and withdrawals. Data for each outcome were synthesized by a Bayesian network meta-analysis (NMA). For efficacy assessments, labelled doses for OA treatment were used for the base case while labelled doses for RA treatment were also included in the sensitivity analysis. Pooled data across dose ranges were used for safety. Efficacy, safety, and tolerability data were found for 146,524 patients in 176 studies included in the NMA. Diclofenac (150 mg/day) was likely to be more effective in alleviating pain than celecoxib (200 mg/day), naproxen (1000 mg/day), and ibuprofen (2400 mg/day), and similar to etoricoxib (60 mg/day); a lower dose of diclofenac (100 mg/day) was comparable to all other treatments in alleviating pain. Improved physical function with diclofenac (100 and 150 mg/day) was mostly comparable to all other treatments. PGA with diclofenac (100 and 150 mg/day) was likely to be more effective or comparable to all other treatments. All active treatments were similar for APTC and major CV events. Major upper GI events with diclofenac was lower compared to naproxen and ibuprofen, comparable to celecoxib, and higher than etoricoxib. Risk of withdrawal with diclofenac was lower compared to ibuprofen, similar to celecoxib and naproxen, and higher than etoricoxib. The benefit-risk profile of diclofenac was comparable to other treatments used for pain relief in OA and RA; benefits and risks vary in individuals and need consideration when making treatment decisions.Arthritis research & therapy 03/2015; 17(1):66. DOI:10.1186/s13075-015-0554-0 · 4.12 Impact Factor
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ABSTRACT: 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. 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. 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.BMJ Open 03/2015; 5(3):e006139. DOI:10.1136/bmjopen-2014-006139 · 2.06 Impact Factor
Article: The meta-analytic big bang[Show abstract] [Hide abstract]
ABSTRACT: This article looks at the impact of meta-analysis and then explores why meta-analysis was developed at the time and by the scholars it did in the social sciences in the 1970s. For the first problem, impact, it examines the impact of meta-analysis using citation network analysis. The impact is seen in the sciences, arts and humanities, and on such contemporaneous developments as multilevel modeling, medical statistics, qualitative methods, program evaluation, and single-case design. Using a constrained snowball sample of citations, we highlight key articles that are either most highly cited or most central to the systematic review network. Then, the article examines why meta-analysis came to be in the 1970s in the social sciences through the work of Gene Glass, Robert Rosenthal, and Frank Schmidt, each of whom developed similar theories of meta-analysis at about the same time. The article ends by explaining how Simonton's chance configuration theory and Campbell's evolutionary epistemology can illuminate why meta-analysis occurred with these scholars when it did and not in medical sciences. Copyright © 2015 John Wiley & Sons, Ltd.02/2015; DOI:10.1002/jrsm.1132