Stakeholder Perspectives on a Risk-Benefit Framework for Genetic Testing

Abstract

A key to accelerating the appropriate integration of genomic applications into healthcare in the coming decades will be the ability to assess the tradeoffs between clinical benefits and clinical risks of genetic tests in a timely manner. Several factors limit the ability of stakeholders to achieve this objective, including the lack of direct evidence, the lack of a framework to quantitatively assess risk and benefit, and the lack of a formal analytic approach to assess uncertainty. We propose that a formal, quantitative risk-benefit framework may be particularly useful for assessing genetic tests intended to influence health outcomes, and communicating the potential clinical benefits, harms, and uncertainty to stakeholders. As part of the development process for such a framework, a stakeholder meeting was held in Seattle (Wash., USA) in December of 2008, with the objective of discussing a risk-benefit framework, using warfarin pharmacogenomics as a case study. Participants engaged in focused discussion to elucidate the potential role of genetic test risk-benefit analysis in informing decision-making, categorizing genetic tests and directing research prioritization. This research investigation focuses on qualitative analysis of responses elicited from workshop participants during the proceedings of the workshop session. The major findings of the workshop were: (1) stakeholder support for risk-benefit modeling as a tool to structure discussion of the clinical utility of genetic tests; (2) desire for the modeling process to be iterative, transparent, and parsimonious in its presentation to stakeholders, and (3) some concern with the use of quality-adjusted life-years in the evaluation process. The meeting’s findings emphasize the potential utility of risk-benefit analysis in genetic test evaluation, and highlight key areas for future research and stakeholder consensus-building.

Regulatory Benefit-Risk Assessment and Comparative Effectiveness Research

“…In a previous study, we assessed whether clinical guideline developers and other stakeholders would find benefit-risk modelling a useful adjunct in their deliberations. [35] We found that there is support for a benefit-risk modelling framework as a decision tool, providing for a more structured and transparent consideration. However, there remains some scepticism and debate about the usefulness of the QALY metric.…”

Stakeholder perspectives on decision-analytic modeling frameworks to assess genetic services policy

“…4 That study involved a wider range of stakeholders, including consumers, developers, payers, clinicians, and researchers. Our current study included a less-diverse group that had a higher representation of genetic services decision-makers.…”

“…12 Drawing from previous research, 4 we identified three key content area codes ( Table 2) to frame the survey questions and stakeholder feedback elicitation and used this scheme to evaluate our notes of the participants' discussion.…”

“…These tests typically become available to patients and clinicians before their clinical utility is fully understood; [1][2][3] thus, a key challenge in this decision-making process is assessing the available evidence on how genetic testing affects health outcomes. 4 Patient-level studies of clinical utility may be lengthy and prohibitively expensive. 5,6 Therefore, genetic services stakeholders must often make coverage decisions in the absence of complete and reliable evidence of clinical utility and under budget constraints.…”

Economic Perspectives on Personalized Health Care and Prevention

“…However, he did note that costs are intertwined with health outcomes and thus may be considered as relevant. Stakeholder groups have noted discomfort with using quality-adjusted life years (QALYs) because they do not capture all relevant factors and because of concerns about the methods used to obtain them (Roth et al 2011). Furthermore, Greenberg and Neumann (2011) found that adjusting life years for quality of life does not substantively affect cost per life year ratios, at least for cancerrelated interventions.…”