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Aims To compare the cardiovascular efficacy and safety of sodium‐glucose co‐transporter‐2 (SGLT2) inhibitors and glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) in adults with Type 2 diabetes. Methods Electronic databases were searched from inception to 22 October 2018 for randomized controlled trials designed to assess the cardiovascular efficacy of SGLT2 inhibitors or GLP‐1RAs with regard to a three‐point composite measure of major adverse cardiovascular events (non‐fatal stroke, non‐fatal myocardial infarction and cardiovascular mortality). Cardiovascular and safety data were synthesized using Bayesian network meta‐analyses. Results Eight trials, including 60 082 participants, were deemed eligible for the network meta‐analysis. Both SGLT2 inhibitors [hazard ratio 0.86 (95% credible interval 0.74, 1.01]) and GLP‐1RAs [hazard ratio 0.88 (95% credible interval 0.78, 0.98)] reduced the three‐point composite measure compared to placebo, with no evidence of differences between them [GLP‐1RAs vs SGLT2 inhibitors: hazard ratio 1.02 (95% credible interval 0.83, 1.23)]. SGLT2 inhibitors reduced risk of hospital admission for heart failure compared to placebo [hazard ratio 0.67 (95% credible interval 0.53, 0.85)] and GLP‐1RAs [hazard ratio 0.71 (95% credible interval 0.53, 0.93)]. No differences were found between the two drug classes in non‐fatal stroke, non‐fatal myocardial infarction, cardiovascular mortality, all‐cause mortality or safety outcomes. Conclusions SGLT2 inhibitors and GLP‐1RAs reduced the three‐point major adverse cardiovascular event risk compared to placebo, with no differences between them. Compared with GLP‐1RAs and placebo, SGLT2 inhibitors led to a larger reduction in hospital admission for heart failure risk.

Background Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod. Methods A range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. Results Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. ‘Power prior’ NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty. Conclusion The ‘power prior’ method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.

Background There is a growing interest in the inclusion of real-world and observational studies in evidence synthesis such as meta-analysis and network meta-analysis in public health. While this approach offers great epidemiological opportunities, use of such studies often introduce a significant issue of double-counting of participants and databases in a single analysis. Therefore, this study aims to introduce and illustrate the nuances of double-counting of individuals in evidence synthesis including real-world and observational data with a focus on public health. Methods The issues associated with double-counting of individuals in evidence synthesis are highlighted with a number of case studies. Further, double-counting of information in varying scenarios is discussed with potential solutions highlighted. Results Use of studies of real-world data and/or established cohort studies, for example studies evaluating the effectiveness of therapies using health record data, often introduce a significant issue of double-counting of individuals and databases. This refers to the inclusion of the same individuals multiple times in a single analysis. Double-counting can occur in a number of manners, such as, when multiple studies utilise the same database, when there is overlapping timeframes of analysis or common treatment arms across studies. Some common practices to address this include synthesis of data only from peer-reviewed studies, utilising the study that provides the greatest information (e.g. largest, newest, greater outcomes reported) or analysing outcomes at different time points. Conclusions While common practices currently used can mitigate some of the impact of double-counting of participants in evidence synthesis including real-world and observational studies, there is a clear need for methodological and guideline development to address this increasingly significant issue.

IntroductionSodium-glucose cotransporter 2 inhibitors (SGLT-2is) and glucagon-like peptide-1 receptor agonists (GLP-1RAs) are two classes of glucose-lowering drugs gaining popularity in the treatment of type 2 diabetes mellitus (T2DM). Current guidelines suggest patient-centred approaches when deciding between available hyperglycaemia drugs with no indication to which specific drug should be administered. Despite systematic reviews and meta-analyses being conducted within SGLT-2is and GLP-1RAs, differences across these classes of drugs have not been investigated. Therefore, this systematic review and network meta-analysis (NMA) will aim to compare the efficacy and safety profiles across and within SGLT-2is and GLP-1RAs.MethodsPubMed, the Cochrane Central Register of Controlled Trials and ISI Web of Science will be searched from inception for published randomised controlled trials conducted in patients with T2DM, with at least two arms consisting of SGLT-2is, GLP-1RAs or control/placebo. Title and abstracts will be screened by two independent reviewers with conflicts resolved by a third. Data will be extracted by the primary researcher, a random sample will be checked by an independent reviewer. Risk of bias will be assessed using the Cochrane Risk of Bias Tool and overall quality of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach.Study characteristics, participants baseline characteristics, mean change in cardiometabolic outcomes and number of adverse events will be extracted for each study. Primary outcome will be the mean change in glycated haemoglobin (HbA1c) (%, mmol/mol). Initial random-effects pairwise meta-analysis will be conducted for each unique treatment comparison where heterogeneity will be assessed. A Bayesian NMA approach will be adopted where random-effects generalised linear models will be fitted in WinBUGS. Sensitivity analysis will be conducted to assess choices of prior distributions and length of burn-in and sample.Ethics and disseminationEthics approval is not required for this study. Results from this study will be published in a peer-review journal.PROSPERO registration numberCRD42018091306.

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IntroductionThe use of real-world data, as an alternative to randomized controlled trials, is becoming increasingly common in the evaluation of new health technologies. With this rise in real-world literature, such data will also enter evidence synthesis models. While it can be beneficial to utilize data from all available sources, this can introduce the problem of double-counting of participants.MethodsUsing a number of case-studies, we discuss and illustrate various issues around double-counting. These include synthesis of studies using the same database or the same subset of participants, overlapping use of intervention arms across studies and the use of registry data from the participants overlapping with those in randomized controlled trials. The implications in research are considered along with common methods used currently to overcome these issues.ResultsDouble-counting of participants in evidence synthesis can artificially inflate precision, potentially leading to inappropriate conclusions. Common methods currently used to help mitigate the impact of double-counting includes stratifying analysis to different timelines, using the most comprehensive study in the evidence synthesis model or using the study that has the largest sample size. However, in all of these cases, sensitivity analyses would need to be considered to ensure robust results.ConclusionsCurrently, there are no published guidelines on how to address the issue of double-counting. With the increased use of real-world data in evidence synthesis, double-counting has the potential to become a significant issue. Therefore, it is of significant importance that methodologies and guidelines are developed to address this.