The economic value of personalized medicine tests: what we know and what we need to know.
OBJECTIVE
There is uncertainty about when personalized medicine tests provide economic value. We assessed evidence on the economic value of personalized medicine tests and gaps in the evidence base.


METHODS
We created a unique evidence base by linking data on published cost-utility analyses from the Tufts Cost-Effectiveness Analysis Registry with data measuring test characteristics and reflecting where value analyses may be most needed: (i) tests currently available or in advanced development, (ii) tests for drugs with Food and Drug Administration labels with genetic information, (iii) tests with demonstrated or likely clinical utility, (iv) tests for conditions with high mortality, and (v) tests for conditions with high expenditures.


RESULTS
We identified 59 cost-utility analyses studies that examined personalized medicine tests (1998-2011). A majority (72%) of the cost/quality-adjusted life year ratios indicate that testing provides better health although at higher cost, with almost half of the ratios falling below $50,000 per quality-adjusted life year gained. One-fifth of the results indicate that tests may save money.


CONCLUSIONS
Many personalized medicine tests have been found to be relatively cost-effective, although fewer have been found to be cost saving, and many available or emerging medicine tests have not been evaluated. More evidence on value will be needed to inform decision making and assessment of genomic priorities.
1] Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, California, USA [2] UCSF Philip R. Lee Institute for Health Policy, San Francisco, California, USA [3] UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, California, USA [4] UCSF Institute for Human Genetics, Center for Business Models in Healthcare, Chicago, Illinois, USA.
Kathryn A
KA
Phillips
Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, California, USA.
Julie
J
Ann Sakowski
1] Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, California, USA [2] UCSF Institute for Human Genetics, Center for Business Models in Healthcare, Chicago, Illinois, USA.
Julia
J
Trosman
1] Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, California, USA [2] McKing Consulting Corporation, Fairfax, Virginia, USA.
Michael P
MP
Douglas
1] Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, California, USA [2] Palo Alto Medical Foundation Research Institute, Palo Alto, California, USA.
Su-Ying
SY
Liang
Center for Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA.
Peter
P
Neumann
D003362:Cost-Benefit Analysis; D005820:Genetic Testing / Q000191:economics* / Q000379:methods; D006801:Humans; D057285:Precision Medicine / Q000191:economics* / Q000379:methods; D019057:Quality-Adjusted Life Years; D012042:Registries / Q000706:statistics & numerical data*; D014481:United States