A framework for organizing and selecting quantitative approaches for benefit-harm assessment

Abstract

Background: Several quantitative approaches for benefit-harm assessment of health care interventions exist but it is unclear how the approaches differ. Our aim was to review existing quantitative approaches for benefit-harm assessment and to develop an organizing framework that clarifies differences and aids selection of quantitative approaches for a particular benefit-harm assessment.

Methods: We performed a review of the literature to identify quantitative approaches for benefit-harm assessment. Our team, consisting of clinicians, epidemiologists, and statisticians, discussed the approaches and identified their key characteristics. We developed a framework that helps investigators select quantitative approaches for benefit-harm assessment that are appropriate for a particular decisionmaking context.

Results: Our framework for selecting quantitative approaches requires a concise definition of the treatment comparison and population of interest, identification of key benefit and harm outcomes, and determination of the need for a measure that puts all outcomes on a single scale (which we call a benefit and harm comparison metric). We identified 16 quantitative approaches for benefit-harm assessment. These approaches can be categorized into those that consider single or multiple key benefit and harm outcomes, and those that use a benefit-harm comparison metric or not. Most approaches use aggregate data and can be used in the context of single studies or systematic reviews. Although the majority of approaches provides a benefit and harm comparison metric, only four approaches provide measures of uncertainty around the benefit and harm comparison metric (such as a 95 percent confidence interval). None of the approaches considers the actual joint distribution of benefit and harm outcomes, but one approach considers competing risks when calculating profile-specific event rates. Nine approaches explicitly allow incorporating patient preferences.

Conclusion: The choice of quantitative approaches depends on the specific question and goal of the benefit-harm assessment as well as on the nature and availability of data. In some situations, investigators may identify only one appropriate approach. In situations where the question and available data justify more than one approach, investigators may want to use multiple approaches and compare the consistency of results. When more evidence on relative advantages of approaches accumulates from such comparisons, it will be possible to make more specific recommendations on the choice of approaches.

Figures

Figure 1

Key characteristics of the benefit-harm question that may guide selection of quantitative assessment for benefit-harm assessment. Abbreviations: INHB, Incremental net health benefit; MCE, Minimum clinical efficacy; NCB, Net Clinical Benefit; NNT, Number needed to treat; NNH, Number needed to treat for harm; Q-Twist, (Quality-adjusted) Time without Symptoms and Toxicity: RBC, Risk–benefit contour; RV-NNT, Relative value adjusted number needed to treat; QFRBA, Quantitative Framework for Risk and Benefit Assessment; TURBO, Transparent Uniform Risk Benefit; BLRA, Benefit-less-risk analysis; PSM, Probabilistic simulation methods; MERT, Minimum Target Event Risk for Treatment; MCDA, Multicriteria decision analysis: RBP, Risk–benefit plane; SPM, Stated preference method; MAR, Maximum acceptable risk.