Using "Big Data" in the Cost-Effectiveness Analysis of Next-Generation Sequencing Technologies: Challenges and Potential Solutions

Next-generation sequencing (NGS) is considered to be a prominent example of "big data" because of the quantity and complexity of data it produces and because it presents an opportunity to use powerful information sources that could reduce clinical and health economic uncertainty at a patient level. One obstacle to translating NGS into routine health care has been a lack of clinical trials evaluating NGS technologies, which could be used to populate cost-effectiveness analyses (CEAs). A key question is whether big data can be used to partially support CEAs of NGS. This question has been brought into sharp focus with the creation of large national sequencing initiatives. In this article we summarize the main methodological and practical challenges of using big data as an input into CEAs of NGS. Our focus is on the challenges of using large observational datasets and cohort studies and linking these data to the genomic information obtained from NGS, as is being pursued in the conduct of large genomic sequencing initiatives. We propose potential solutions to these key challenges. We conclude that the use of genomic big data to support and inform CEAs of NGS technologies holds great promise. Nevertheless, health economists face substantial challenges when using these data and must be cognizant of them before big data can be confidently used to produce evidence on the cost-effectiveness of NGS.