Table 3 –
Benefit-Risk Terminology
| Benefit | The intended positive or favorable effects of an intervention for the target population (often referred to as “benefits” or “clinical benefits”) that are associated with an intervention [36]. Examples include reduction in pain intensity, increase in number of pain free days, function, and quality of life. |
| Risk | The unintended negative clinical and health outcomes or detrimental effects that can be attributed to the intervention. The use of the term risk in the present article includes side effects, some of which will have an adverse effect on patient functioning, but also includes safety risks, SAEs such as myocardial infarction, or death. The intensity and duration of all treatment-emergent AEs should be collected (total, severe, and serious), as well as the use of active capture, which includes interviews or questionnaires [36; 73]. |
| Benefit-Risk Assessment | A structured method (qualitative or quantitative) for combining separate benefit and risk outcomes into a composite metric that allows for a clear comparison of benefits and risks in relation to each other at the level of the group or for individual patients. |
| Clinical Utility | The ability of a clinical test result(s) to inform a decision that positively changes the outcome of a patient [144] |
| Qualitative Framework | Qualitative or descriptive frameworks provide stepwise instructions for evaluating and balancing benefit and risk, including their frequency and duration, and fully describes how that information weighs into decision making [123]. Examples include: The Problem formulation, Objectives, Alternatives, Consequences, Trade-offs, Uncertainties, Risk attitude, and Linked decisions framework and the United States Food and Drug Administration Benefit-Risk Framework. |
| Quantitative Framework | Quantitative frameworks provide explicit methods for combining and weighing risks and benefits. A quantitative approach may help to improve the transparency of a review, relative to a qualitative approach, by being explicit about how benefits and harms are estimated and compared (Boyd et al., 2012). While quantitative approaches can be used to examine benefit-risk at the level of the group, there are most commonly used for analyses that begin at the level of the individual patient (Table 1). Examples include multiple-criteria decision analysis (MCDA), discrete-event simulation, probabilistic simulation, and Bayesian belief networks [111; 112]. |
| Patient Preferences | Patient preferences represent patient’s attitudes toward a set of alternatives necessary for decision-making [77]. Collecting data related to a patient’s perspective or preference should be taken into account at all stages of research including planning of the clinical trial design and the identification of patient-relevant outcomes [13; 81; 82; 151]. |
| Standardization and Transparency | A systematic and transparent evaluation process that allows for consistency of reporting, replication, and pooling of data across studies [73]. |
Note: AE (adverse event); SAE (serious adverse event).