gms | German Medical Science

Background/research question: When making evidence-based treatment recommendations in clinical practice, there is often considerable uncertainty regarding the expected effects of interventions for certain patient subgroups or individual patients. While calls for more trials frequently emerge in such situations, the efforts and costs associated with conducting further trials need to be balanced against their expected additional value. The conduct of cumulative meta-analyses, or trial sequential meta-analyses, has been propagated as a means to investigate the available evidence over time and make decisions about whether further trials are needed. Previous research by our group has shown that quantitative benefit-harm assessments (BHAs) may be useful for setting research priorities by transparently and simultaneously considering both the expected benefits and potential harms of an intervention.

Methods: We aim to investigate whether combining cumulative meta-analysis and quantitative BHA into a cumulative quantitative BHA could provide additional benefits over either method alone. We will use two examples from a preventive setting: low-dose aspirin and statins for the primary prevention of cardiovascular disease. For both interventions separately, we will combine cumulative meta-analysis with a probabilistic mathematical modelling approach to derive an index for the balance of benefits and harms of either treatment at the time of publication of each trial. Weights for benefit and harm outcomes will be drawn from previously conducted studies on patient preferences in these settings. In both contexts, we will investigate the extent to which the estimated benefit-harm balance changed with each additional trial and how this could have changed recommendations.

Preliminary/expected results, outlook: We expect this research to substantially contribute to the current understanding on how cumulative quantitative BHAs might contribute to setting research priorities in medical research. This innovative method will enable an examination of the benefit-harm balance of interventions with changing evidence over time and making judgements about the usefulness of further trials. By additionally considering the impact of patient preferences, this novel method might provide an essential step towards conducting more patient-relevant research. We expect cumulative quantitative BHA to have significant potential in setting research priorities by linking comparative effectiveness research with preference-informed BHA.

Competing interests: We declare to have no conflicts of interest.