Display options
Cite
Alemi F, Erdman H, Griva I, et al. Improved Statistical Methods are Needed to Advance Personalized Medicine. Open Transl Med J. 2009;1:16-20doi: 10.2174/1876399500901010016.
Alemi, F., Erdman, H., Griva, I., & Evans, C. H. (2009). Improved Statistical Methods are Needed to Advance Personalized Medicine. The open translational medicine journal, 116-20. https://doi.org/10.2174/1876399500901010016
Alemi, Farrokh, et al. "Improved Statistical Methods are Needed to Advance Personalized Medicine." The open translational medicine journal vol. 1 (2009): 16-20. doi: https://doi.org/10.2174/1876399500901010016
Alemi F, Erdman H, Griva I, Evans CH. Improved Statistical Methods are Needed to Advance Personalized Medicine. Open Transl Med J. 2009 Jan 01;1:16-20. doi: 10.2174/1876399500901010016. PMID: 20676226; PMCID: PMC2911789.
Copy Download .nbib Format: NLM
Share it on

Open Transl Med J. 2009 Jan 01;1:16-20. doi: 10.2174/1876399500901010016.

Improved Statistical Methods are Needed to Advance Personalized Medicine.

The open translational medicine journal

Farrokh Alemi, Harold Erdman, Igor Griva, Charles H Evans

Affiliations

  1. Department of Health System Administration, School of Nursing and Health Studies, Georgetown University Medical Center, 3700 Reservoir Rd NW, Washington DC 20057, USA.

PMID: 20676226 PMCID: PMC2911789 DOI: 10.2174/1876399500901010016

Abstract

Common methods of statistical analysis, e.g. Analysis of Variance and Discriminant Analysis, are not necessarily optimal in selecting therapy for an individual patient. These methods rely on group differences to identify markers for disease or successful interventions and ignore sub-group differences when the number of sub-groups is large. In these circumstances, they provide the same advice to an individual as the average patient. Personalized medicine needs new statistical methods that allow treatment efficacy to be tailored to a specific patient, based on a large number of patient characteristics. One such approach is the sequential k-nearest neighbor analysis (patients-like-me algorithm). In this approach, the k most similar patients are examined sequentially until a statistically significant conclusion about the efficacy of treatment for the patient-at-hand can be arrived at. For some patients, the algorithm stops before the entire set of data is examined and provides beneficial advice that may contradict recommendations made to the average patient. Many problems remain in creating statistical tools that can help individual patients but this is an important area in which progress in statistical thinking is helpful.

References

  1. Qual Manag Health Care. 2007 Apr-Jun;16(2):130-4 - PubMed
  2. Curr Clin Pharmacol. 2007 Jan;2(1):11-21 - PubMed
  3. Nature. 2008 Apr 3;452(7187):564-70 - PubMed
  4. Qual Manag Health Care. 2008 Jan-Mar;17(1):66-79 - PubMed
  5. Psychiatry Res. 2006 Feb 28;141(2):193-200 - PubMed
  6. J Biopharm Stat. 2008;18(5):853-68 - PubMed
  7. Dialogues Clin Neurosci. 2008;10(4):401-8 - PubMed
  8. Mem Cognit. 2004 Apr;32(3):379-89 - PubMed
  9. Bioinform Biol Insights. 2008;2:15-23 - PubMed
  10. Crit Care Med. 2009 Jan;37(1 Suppl):S50-8 - PubMed
  11. Stat Methodol. 2008;5(3):238-262 - PubMed
  12. J Exp Psychol Gen. 1990 Mar;119(1):30-41 - PubMed
  13. J Asthma. 2008 May;45(4):257-64 - PubMed
  14. Clin Chim Acta. 2009 Feb;400(1-2):25-9 - PubMed
  15. Qual Manag Health Care. 2005 Oct-Dec;14(4):217-8 - PubMed
  16. Sci Am. 2009 Feb;300(2):44-51 - PubMed

Publication Types

Grant support