This insightful article, authored by Jeff J. Guo and colleagues, provides a comprehensive review of quantitative risk-benefit assessment (RBA) methodologies specifically tailored for pharmaceuticals. Published in Value in Health in 2010, the paper addresses a critical need within the pharmaceutical industry and regulatory landscape: the lack of consistent and integrated quantitative RBA during the new drug approval process, despite regulatory authorities routinely evaluating drug risks and benefits.
The context for this review is significant. The past decade witnessed the withdrawal of over a dozen high-profile brand-name drugs from the market, prompting a renewed global focus on drug safety. Regulatory bodies like the US Food and Drug Administration (FDA) and the European Medicines Agency (EMEA) have actively sought more creative and systematic approaches to RBA throughout a pharmaceutical product’s life cycle. The FDA established a Drug Safety and Risk Management Division and issued comprehensive risk management guidance, while the EMEA’s Committee for Medicinal Products for Human Use (CHMP) revised its guidance to include quantitative RBA in its regulatory agenda. The authors emphasize that appropriate RBA can lead to proactive interventions, saving lives, reducing litigation, improving patient safety and health outcomes, and lowering overall healthcare costs.
The primary objective of this study was to identify and describe published quantitative RBA methods for pharmaceuticals, summarizing their distinct approaches and highlighting their implications for the industry and regulatory agencies. To achieve this, the researchers conducted a systematic literature review using databases such as MEDLINE, Cochrane Library, and other internet-based search engines. From an initial pool of over 12,000 papers, the review ultimately focused on 59 articles detailing quantitative RBA methods for drug safety.
The article thoroughly reviews and compares twelve distinct quantitative RBA methodologies, providing details on their theoretical models, parameters, and key features:
- Quantitative Framework for Risk and Benefit Assessment (QFRBA)
- Benefit-Less-Risk Analysis (BLRA)
- Quality-Adjusted Time Without Symptoms and Toxicity (Q-TWiST)
- Number Needed to Treat (NNT) and Number Needed to Harm (NNH), including their relative-value-adjusted versions
- Minimum Clinical Efficacy (MCE)
- Incremental Net Health Benefit (INHB)
- Risk–Benefit Plane (RBP)
- Probabilistic Simulation Methods (PSM) and Monte Carlo Simulation (MCS)
- Multicriteria Decision Analysis (MCDA)
- Risk–Benefit Contour (RBC)
- Stated Preference Method (SPM)
The authors note that these methods vary in their approach; for example, some (like NNT) rely on subjective weighting schemes, while others (such as RBP, MCDA, RBC, and SPM) are capable of assessing joint distributions of benefit and risk.
In conclusion, the review asserts that these scientifically sound quantitative RBA methodologies offer a pathway to reduce the subjective nature of current drug assessments, thereby guiding regulatory agencies towards more objective, transparent, and evidence-based decision-making. While these methods have primarily been used for research demonstrations and are not yet systematically adopted by regulators or the pharmaceutical industry, they hold significant potential as supplementary tools. The authors recommend the use of multiple RBA approaches across different therapeutic indications and patient populations to comprehensively bound a drug’s risk-benefit profile. Despite their promise, common limitations include challenges with data requirements, statistical properties, and the availability of valid patient preference measures. Among the reviewed methods, INHB, MCDA, and SPM have garnered particular attention from key regulatory bodies like the FDA and EMEA.
Reference: Guo, J. J., Pandey, S., Doyle, J., Bian, B., Lis, Y., & Raisch, D. W. (2010). A Review of Quantitative Risk–Benefit Methodologies for Assessing Drug Safety and Efficacy—Report of the ISPOR Risk–Benefit Management Working Group. Value in Health, 13(6), 657–666.
