Sofia Dias will talk about using "all the evidence" for decision making and give some examples of methods starting from network meta-analysis and then looking at refinements such as dose-response models. This will be preceded by our AGM.
The RSS Sheffield Local Group will be hosting our last event of the year online, our AGM followed by a webinar from Sofia Dias.
Evidence synthesis for decision making: making best use of relevant evidence.
Meta-analyses are typically used to pool evidence from multiple studies in order to decide which treatment is most effective or cost-effective, out of several alternatives. When deciding which treatments to recommend for use in a national health service, we typically start with a well-defined decision problem specifying the patient population, interventions and outcomes of interest. A search of the literature for randomised controlled trials (RCTs) comparing the interventions of interest then follows, where evidence is collected and assessed for quality and relevance to the decision problem.
Often evidence from available RCTs that does not exactly match our decision problem is classed as not directly applicable to the decision-problem (indirect evidence) and discarded. However, models that allow incorporation of such "indirect" evidence using reasonable assumptions may reduce uncertainty in estimates of treatment effectiveness, leading to better decisions.
Network meta-analysis (NMA) extended the idea of pairwise meta-analysis to pool evidence on more than one intervention, allowing for indirect evidence on additional treatment comparisons to be incorporated. Whilst standard NMA methods are now well established, some recent extensions allow pooling of additional data, reducing uncertainty.
After briefly introducing the principles of meta-analysis and NMA, the extension of NMA models to incorporate dose-response relationships will be described. Using examples, we will show how evidence on different doses of interventions can be combined to strengthen inferences and how key modelling assumptions can be checked.
Some additional methodological extensions that allow other types of "indirect" evidence to be incorporated will also be outlined.