Reka E Pataky1,2, Stirling Bryan3,4, Mohsen Sadatsafavi5, Stuart Peacock6,7, Dean A Regier6,3. 1. Canadian Centre for Applied Research in Cancer Control, Cancer Control Research, BC Cancer Research Centre, 675 W. 10th Ave, Vancouver, BC, V5Z 1L3, Canada. rpataky@bccrc.ca. 2. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. rpataky@bccrc.ca. 3. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. 4. Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada. 5. Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada. 6. Canadian Centre for Applied Research in Cancer Control, Cancer Control Research, BC Cancer Research Centre, 675 W. 10th Ave, Vancouver, BC, V5Z 1L3, Canada. 7. Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
Abstract
BACKGROUND AND OBJECTIVE: Precision medicine highlights the importance of exploring heterogeneity in the effectiveness and costs of interventions. Our objective was to identify and compare frameworks for valuing heterogeneity-informed decisions, and consider their strengths and weaknesses for application to precision medicine. METHODS: We conducted a scoping review to identify papers that proposed an analytical framework to place a value, in terms of costs and health benefits, on using heterogeneity to inform treatment selection. The search included English-language papers indexed in MEDLINE, Embase or EconLit, and a manual review of references and citations. We compared the frameworks qualitatively considering: the purpose and setting of the analysis; the types of precision medicine interventions where the framework could be applied; and the framework's ability to address the methodological challenges of evaluating precision medicine. RESULTS: Four analytical frameworks were identified: value of stratification, value of heterogeneity, expected value of individualised care and loss with respect to efficient diffusion. Each framework is suited to slightly different settings and research questions. All focus on maximising net benefit, and quantify the opportunity cost of ignoring heterogeneity by comparing individualised or stratified decisions to a means-based population-wide decision. Where the frameworks differ is in their approaches to uncertainty, and in the additional metrics they consider. CONCLUSIONS: Identifying and utilising heterogeneity is at the core of precision medicine, and the ability to quantify the value of heterogeneity-informed decisions is critical. Using an analytical framework to value heterogeneity will help provide evidence to inform investment in precision medicine interventions, appropriately capturing the value of targeted health interventions.
BACKGROUND AND OBJECTIVE: Precision medicine highlights the importance of exploring heterogeneity in the effectiveness and costs of interventions. Our objective was to identify and compare frameworks for valuing heterogeneity-informed decisions, and consider their strengths and weaknesses for application to precision medicine. METHODS: We conducted a scoping review to identify papers that proposed an analytical framework to place a value, in terms of costs and health benefits, on using heterogeneity to inform treatment selection. The search included English-language papers indexed in MEDLINE, Embase or EconLit, and a manual review of references and citations. We compared the frameworks qualitatively considering: the purpose and setting of the analysis; the types of precision medicine interventions where the framework could be applied; and the framework's ability to address the methodological challenges of evaluating precision medicine. RESULTS: Four analytical frameworks were identified: value of stratification, value of heterogeneity, expected value of individualised care and loss with respect to efficient diffusion. Each framework is suited to slightly different settings and research questions. All focus on maximising net benefit, and quantify the opportunity cost of ignoring heterogeneity by comparing individualised or stratified decisions to a means-based population-wide decision. Where the frameworks differ is in their approaches to uncertainty, and in the additional metrics they consider. CONCLUSIONS: Identifying and utilising heterogeneity is at the core of precision medicine, and the ability to quantify the value of heterogeneity-informed decisions is critical. Using an analytical framework to value heterogeneity will help provide evidence to inform investment in precision medicine interventions, appropriately capturing the value of targeted health interventions.
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