Precision medicine approaches and the health of populations: Study design concerns and considerations

Perspectives in Biology and Medicine

Volumn 61, Issue 4, 2018, Pages 527-536

  Galea, Sandro ^a   Abdalla, Salma M ^a  

^a BOSTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; LOW DENSITY LIPOPROTEIN CHOLESTEROL;

ARTIFICIAL INTELLIGENCE; BIOLOGY; BLOOD; CLINICAL TRIAL (TOPIC); ELECTRONIC HEALTH RECORD; HUMAN; MOBILE PHONE; OBSERVATIONAL STUDY; PERSONALIZED MEDICINE; PHARMACOGENETIC TESTING; PROCEDURES; SELECTION BIAS; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICS AND NUMERICAL DATA;

ARTIFICIAL INTELLIGENCE; BIOMARKERS; CELL PHONE; CHOLESTEROL, LDL; CLINICAL TRIALS AS TOPIC; COMPUTATIONAL BIOLOGY; ELECTRONIC HEALTH RECORDS; HUMANS; OBSERVATIONAL STUDIES AS TOPIC; PHARMACOGENOMIC TESTING; POLYMORPHISM, SINGLE NUCLEOTIDE; PRECISION MEDICINE; SELECTION BIAS;

EID: 85059493084     PISSN: 00315982     EISSN: None     Source Type: Journal    
DOI: 10.1353/pbm.2018.0062     Document Type: Article

References (18)

1. Alyass, A., M. Turcotte, and D. Meyre. 2015. “From Big Data Analysis to Personalized Medicine for All: Challenges and Opportunities.” BMC Med Genomics 8 (1): 33. DOI: 10.1186/s12920-015-0108-y.
2. Collins, F. S., and H. Varmus. 2015. “A New Initiative on Precision Medicine.” N Engl J Med 372 (9): 793–95. DOI: 10.1056/NEJMp1500523.
3. Evenson, K. R., M. M. Goto, and R. D. Furberg. 2015. “Systematic Review of the Validity and Reliability of Consumer-Wearable Activity Trackers.” Intern Behav Nutr Phys Activ 12 (1): 159. DOI: 10.1186/s12966-015-0314-1.
4. Fadista, J., and C. Bendixen. 2012. “Genomic Position Mapping Discrepancies of Commercial SNP Chips.” PLoS One 7 (2): e31025. DOI: 10.1371/journal.pone.0031025.
5. Freidlin, B., L. M. McShane, and E. L. Korn. 2010. “Randomized Clinical Trials With Biomarkers: Design Issues.” JNCI 102 (3): 152–60. DOI: 10.1093/jnci/djp477.
6. Fry, A., et al. 2017. “Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population.” Epidemiology 186 (9): 1026–34. DOI: 10.1093/aje/kwx246.
7. Gomez-Cabrero, D., et al. 2014. “Data Integration in the Era of Omics: Current and Future Challenges.” BMC Systems Biol 8 (suppl. 2): I1. DOI: 10.1186/1752-0509-8-S2-I1.
8. Kaplan, R. M., D. A. Chambers, and R.E. Glasgow. 2014. “Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias.” Clin Transl Sci 7 (4): 342–46. DOI: 10.1111/cts.12178.
9. Khoury, M. J., and S. Galea. 2016. “Will Precision Medicine Improve Population Health?” JAMA 316 (13): 1357. DOI: 10.1001/jama.2016.12260.
10. Marx, V. 2013. “Biology: The Big Challenges of Big Data.” Nature 498: 7453.
11. Mirnezami, R., J. Nicholson, and A. Darzi. 2012. “Preparing for Precision Medicine.” N Engl J Med 366 (6): 489–91. DOI: 10.1056/NEJMp1114866.
12. National Institutes of Health (NIH). 2018. “All of Us.” https://allofus.nih.gov/.
13. Robiou-du-Pont, S., et al. 2015. “Should We Have Blind Faith in Bioinformatics Software? Illustrations from the SNAP Web-Based Tool.” PloS One 10 (3): e0118925. DOI: 10.1371/journal.pone.0118925.
14. Shah, N.D., E.W. Steyerberg, and D.M. Kent. 2018. “Big Data and Predictive Analyt-ics.” JAMA. DOI: 10.1001/jama.2018.5602.
15. Valikodath, N.G., et al. 2017. “Agreement of Ocular Symptom Reporting Between Pa-tient-Reported Outcomes and Medical Records.” JAMA Ophthalmology 135 (3): 225. DOI: 10.1001/jamaophthalmol.2016.5551.
16. Vargas, A.J., and C.C. Harris. 2016. “Biomarker Development in the Precision Medicine Era: Lung Cancer as a Case Study.” Natu Rev Cancer 16 (8): 525–37. DOI:10.1038/nrc.2016.56.
17. Wang, R., et al. 2017. “Accuracy of Wrist-Worn Heart Rate Monitors.” JAMA Cardiology 2 (1): 104. DOI: 10.1001/jamacardio.2016.3340.
18. Zubenko, G.S., B.R. Sommer, and B.M. Cohen. 2018. “On the Marketing and Use of Pharmacogenetic Tests for Psychiatric Treatment.” JAMA Psychiatry. DOI: 10.1001/jamapsychiatry.2018.0834.