Kurt D Christensen1, Kathryn A Phillips2, Robert C Green3, Dmitry Dukhovny4. 1. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. Electronic address: kchristensen@bwh.harvard.edu. 2. Department of Clinical Pharmacy, Center for Translational and Policy Research on Personalized Medicine (TRANSPERS), University of California San Francisco, San Francisco, CA, USA; Philip R. Lee Institute for Health Policy and Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA. 3. Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Partners HealthCare Personalized Medicine, Boston, MA, USA. 4. Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA.
Abstract
OBJECTIVE: To summarize lessons learned while analyzing the costs of integrating whole genome sequencing into the care of cardiology and primary care patients in the MedSeq Project by conducting the first randomized controlled trial of whole genome sequencing in general and specialty medicine. METHODS: Case study that describes key methodological and data challenges that were encountered or are likely to emerge in future work, describes the pros and cons of approaches considered by the study team, and summarizes the solutions that were implemented. RESULTS: Major methodological challenges included defining whole genome sequencing, structuring an appropriate comparator, measuring downstream costs, and examining clinical outcomes. Discussions about solutions addressed conceptual and practical issues that arose because of definitions and analyses around the cost of genomic sequencing in trial-based studies. CONCLUSIONS: The MedSeq Project provides an instructive example of how to conduct a cost analysis of whole genome sequencing that feasibly incorporates best practices while being sensitive to the varied applications and diversity of results it may produce. Findings provide guidance for researchers to consider when conducting or analyzing economic analyses of whole genome sequencing and other next-generation sequencing tests, particularly regarding costs.
OBJECTIVE: To summarize lessons learned while analyzing the costs of integrating whole genome sequencing into the care of cardiology and primary care patients in the MedSeq Project by conducting the first randomized controlled trial of whole genome sequencing in general and specialty medicine. METHODS: Case study that describes key methodological and data challenges that were encountered or are likely to emerge in future work, describes the pros and cons of approaches considered by the study team, and summarizes the solutions that were implemented. RESULTS: Major methodological challenges included defining whole genome sequencing, structuring an appropriate comparator, measuring downstream costs, and examining clinical outcomes. Discussions about solutions addressed conceptual and practical issues that arose because of definitions and analyses around the cost of genomic sequencing in trial-based studies. CONCLUSIONS: The MedSeq Project provides an instructive example of how to conduct a cost analysis of whole genome sequencing that feasibly incorporates best practices while being sensitive to the varied applications and diversity of results it may produce. Findings provide guidance for researchers to consider when conducting or analyzing economic analyses of whole genome sequencing and other next-generation sequencing tests, particularly regarding costs.
Authors: Zornitza Stark; Lena Dolman; Teri A Manolio; Brad Ozenberger; Sue L Hill; Mark J Caulfied; Yves Levy; David Glazer; Julia Wilson; Mark Lawler; Tiffany Boughtwood; Jeffrey Braithwaite; Peter Goodhand; Ewan Birney; Kathryn N North Journal: Am J Hum Genet Date: 2019-01-03 Impact factor: 11.025
Authors: Zoë P Mackay; Dmitry Dukhovny; Kathryn A Phillips; Alan H Beggs; Robert C Green; Richard B Parad; Kurt D Christensen Journal: Value Health Date: 2020-03-20 Impact factor: 5.725