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Using "Big Data" in the Cost-Effectiveness Analysis of Next-Generation Sequencing Technologies: Challenges and
Sarah Wordsworth1, Brett Doble1, Katherine Payne2
1Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Oxford National Institute for Health Research, Biomedical Research Centre, Oxford, UK.
Summary
Genomic big data offers promise for cost-effectiveness analyses (CEAs) of next-generation sequencing (NGS) technologies. However, significant challenges exist in utilizing large observational datasets and linking them with genomic information for robust economic evaluations.
Area of Science:
- Genomics
- Health Economics
- Bioinformatics
Background:
- Next-generation sequencing (NGS) generates substantial "big data," presenting opportunities for personalized medicine.
- Translating NGS into routine healthcare is hindered by a lack of clinical trials for cost-effectiveness analyses (CEAs).
Purpose of the Study:
- To summarize the methodological and practical challenges of using big data in CEAs for NGS technologies.
- To explore the potential of large national sequencing initiatives to inform CEAs.
Main Methods:
- Focus on challenges in using large observational datasets and cohort studies.
- Investigate methods for linking observational data with genomic information from NGS.
- Propose potential solutions to identified challenges.
Main Results:
- Genomic big data holds significant potential to support and inform CEAs of NGS.
- Substantial challenges remain for health economists in confidently using big data for NGS cost-effectiveness evidence.
Conclusions:
- The integration of genomic big data into CEAs for NGS is promising but requires addressing key methodological and practical hurdles.
- Awareness of these challenges is crucial for reliable economic evaluations of NGS technologies.