Periodic Reporting for period 1 - EBAR (Evidence Based Planning of Future Clinical Research)
Reporting period: 2016-06-01 to 2018-05-31
To date, clinical trials that evaluate the efficacy and acceptability of medical interventions are primarily designed as stand-alone experiments ignoring previous evidence. Consequently, researchers potentially experiment with an unnecessarily large number of patients or try to address a question to which the answer is already known.
The aim of EBAR was to develop methods wrapped in a theoretical framework of efficient research planning that has the potential to direct resource allocation by optimally designing future clinical studies after considering the existing evidence. The main methodological vehicle of the suggested framework is the circular updating of a network meta-analysis. Network meta-analysis is a statistical technique to synthesize data from clinical experiments (clinical trials) about the effects of interventions and it is increasingly becoming the norm in evidence synthesis for comparative effectiveness research.
This is described in the attached graph.
Methods underpinning the suggested framework differ from those in conventional network meta-analysis in two major aspects; a) inference about treatment effects is drawn considering the frequent updating of the evidence b) the need for further research and updating are expressed quantitatively via sample-size calculations for future trials. More details about the framework, can be found in the project’s webpage; an online document (http://www.ispm.unibe.ch/research/research_groups/evidence_synthesis_methods/evidence_based_planning_of_clinical_research/index_eng.html#pane680818) a video (https://www.youtube.com/watch?v=0vOAK_YMx_Q#action=share) several talks and lectures, ten published scientific articles and one submitted article. Below we outline the methods and results as presented in the most important developments and findings.
• In a technical article we explained the statistical models needed to continuously update the evidence (in the form of a network meta-analysis). In an empirical follow-up article we showed that continuously updated network meta-analysis (also called “living” network meta-analysis) is the most efficient way to answer clinical questions of policy importance. More specifically, network meta-analysis was 20% more likely to provide strong evidence against the hypothesis of treatment equality than pairwise meta-analysis; also it provided that evidence four years earlier than pairwise meta-analysis. Consequently, prospectively planned living network meta-analysis can facilitate timely recommendations and contribute to reduce research waste by providing strong evidence against the null hypothesis earlier than living pairwise meta-analysis.
• We developed and explained methods of conditional trial design that has the potential to reduce the resources needed to answer a clinical question of relevance to health-policy. We virtually designed a new study to resolve uncertainties about the efficacy of biologics in rheumatoid arthritis using data from an existing NMA as “historical data”. Reduced sample size and flexibility in the randomized arms included are the main advantages of the method. Using data from NMA resulted to reducing more than one third the required sample size compared with using data from pairwise meta-analysis.
• We conducted a survey of trial methodologists about their perceptions for NMA and their opinions about using NMA to design a new clinical trial. This survey found that the level of acceptance of network meta-analysis and it use on designing future studies is moderate. Three out of four participants of the survey were willing to definitely or possibly consider using NMA to design a new clinical trial. However, the major constraint in adopting our framework to plan future studies is the current paradigm of funders of clinical research and stakeholders as it represents a methodological frontier.
We showed that the suggested approach has numerous advantages compared to all other evidence synthesis forms and methods to design clinical trials: increased precisions, timeliness and speeding-up the production of high-quality conclusions about the effects of interventions. Downsides include the greater workload, complex methodology, the need for wider cross-fields collaboration.