title: Assessing cost-utility of predictive biomarkers in oncology: a streamlined approach.
abstract: Evaluation of cost-utility is critical in assessing the medical utility of predictive or prognostic biomarkers. Current methods involve complex state-transition models, requiring comprehensive data inputs. We propose a simplified decision-analytic tool to explore the relative effect of factors contributing to the cost-utility of a biomarker. We derived a cost-utility metric, the "test incremental cost-effectiveness ratio" (TICER) for biomarker-guided treatment compared to no biomarker use. This method uses data inputs readily accessible through clinical literature. We compared our results with traditional cost-effectiveness analysis of predictive biomarkers for established (HER2-guided trastuzumab, ALK-guided crizotinib, OncotypeDX-guided adjuvant chemotherapy) and emerging (ROS1-guided crizotinib) targeted treatments. We conducted sensitivity analysis to determine which factors had the greatest impact on TICER estimates. Base case TICER for HER2 was $149,600/quality-adjusted life year (QALY), for ALK was $22,200/QALY, and for OncotypeDX was $11,600/QALY, consistent with literature-reported estimates ($180,000/QALY, $202,800/QALY, $8900/QALY, respectively). Base case TICER for ROS1-guided crizotinib was $205,900/QALY. Generally, when treatment cost is considerably greater than biomarker testing costs, TICER is driven by clinical outcomes and health-related quality of life, while biomarker prevalence and treatment cost have a lesser effect. Our simplified decision-analytic approach produces values consistent with existing cost-effectiveness analyses. Our results suggest that biomarker value is mostly driven by the clinical efficacy of the targeted agent. A user-friendly web tool for complete TICER analysis has been made available for open use at http://medicine.yale.edu/lab/pusztai/ticer/ .
authors.affiliation: Yale School of Medicine, New Haven, USA.
authors.forename: Anton
authors.initials: A
authors.lastname: Safonov
authors.affiliation: Yale School of Public Health, New Haven, USA.|Cancer Outcomes Public Policy and Effectiveness Research (COPPER) Center, New Haven, USA.
authors.forename: Shiyi
authors.initials: S
authors.lastname: Wang
authors.affiliation: Yale School of Medicine, New Haven, USA.|Cancer Outcomes Public Policy and Effectiveness Research (COPPER) Center, New Haven, USA.|Yale Cancer Center, Yale University, New Haven, USA.
authors.forename: Cary P
authors.initials: CP
authors.lastname: Gross
authors.affiliation: Yale School of Medicine, New Haven, USA.
authors.forename: Divyansh
authors.initials: D
authors.lastname: Agarwal
authors.affiliation: San Raffaele Scientific Institute, Milan, Italy.
authors.forename: Giampaolo
authors.initials: G
authors.lastname: Bianchini
authors.affiliation: Yale School of Medicine, New Haven, USA.|Yale Cancer Center, Yale University, New Haven, USA.
authors.forename: Lajos
authors.initials: L
authors.lastname: Pusztai
authors.affiliation: Yale School of Medicine, New Haven, USA. christos.hatzis@yale.edu.|Yale Cancer Center, Yale University, New Haven, USA. christos.hatzis@yale.edu.
authors.forename: Christos
authors.initials: C
authors.lastname: Hatzis
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D017024:Chemotherapy, Adjuvant / Q000379:methods; D003362:Cost-Benefit Analysis / Q000379:methods; D000077547:Crizotinib; D017048:Health Care Costs; D006801:Humans; D008495:Medical Oncology / Q000191:economics*; D009369:Neoplasms / Q000188:drug therapy / Q000191:economics* / Q000378:metabolism*; D047428:Protein Kinase Inhibitors / Q000191:economics / Q000627:therapeutic use; D011720:Pyrazoles / Q000191:economics / Q000627:therapeutic use; D011725:Pyridines / Q000191:economics / Q000627:therapeutic use; D011788:Quality of Life
chemical_list: D015415:Biomarkers; D047428:Protein Kinase Inhibitors; D011720:Pyrazoles; D011725:Pyridines; D000077547:Crizotinib
keywords: Breast cancer; Comparative effectiveness; Decision analysis; OncotypeDX; Outcomes; Research