Abstract
ObjectivesPrecision oncology is generating vast amounts of multiomic data to improve human health and accelerate research. Existing clinical study designs and attendant data are unable to provide comparative evidence for economic evaluations. This lack of evidence can cause inconsistent and inappropriate reimbursement. Our study defines a core data set to facilitate economic evaluations of precision oncology. MethodsWe conducted a literature review of economic evaluations of next-generation sequencing technologies, a common application of precision oncology, published between 2005 and 2018 and indexed in PubMed (MEDLINE). Based on this review, we developed a preliminary core data set for informal expert feedback. We then used a modified-Delphi approach with individuals involved in implementation and evaluation of precision medicine, including 2 survey rounds followed by a final voting conference to refine the data set. ResultsTwo authors determined that variation in published data elements was reached after abstraction of 20 economic evaluations. Expert consultation refined the data set to 83 unique data elements, and a multidisciplinary sample of 46 experts participated in the modified-Delphi process. A total of 68 elements (81%) were selected as required, spanning demographics and clinical characteristics, genomic data, cancer treatment, health and quality of life outcomes, and resource use. ConclusionsCost-effectiveness analyses will fail to reflect the real-world impacts of precision oncology without data to accurately characterize patient care trajectories and outcomes. Data collection in accordance with the proposed core data set will promote standardization and enable the generation of decision-grade evidence to inform reimbursement.
Highlights
Precision oncology uses multiomic data such as genome and transcriptome analysis to tailor treatment and prevention to individual pathophysiology.[1]
Article selection was limited to evaluations of precision oncology and rare diseases, representing clinical contexts within which nextgeneration sequencing (NGS) has been most frequently applied to research settings.[4]
Results discussed with patients and/or families with the treating physician and genetic counselor
Summary
Precision oncology uses multiomic data such as genome and transcriptome analysis to tailor treatment and prevention to individual pathophysiology.[1] Fundamental to this process is nextgeneration sequencing (NGS), a term for massively parallel DNA sequencing including whole-genome or exome sequencing and multigene panels to identify targetable genomic aberrations and candidate pathways.[2] Despite the ability of NGS to produce rapid results at decreasing cost, clinical use of NGS varies This is partly due to insufficient evidence demonstrating cost-effectiveness, a prerequisite to implementation guidance across jurisdictions.[3,4,5]. Rather than undertake lengthy patient accrual periods powered to detect small effects and account for heterogeneity, investigators often pursue nonrandomized, tumor-agnostic studies powered on short-term outcomes.[8,9] able to support timely reporting, evidence generated from nonrandomized designs is ill equipped to inform robust economic evaluations and corresponding reimbursement decisions
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