Improved Statistical Methods are Needed to Advance Personalized Medicine
Farrokh Alemi*, 1, Harold Erdman1, Igor Griva*
, 2, Charles H. Evans3
1 Department of Health System Administration, School of Nursing and Health Studies, Georgetown University Medical Center, 3700 Reservoir Rd NW, Washington DC 20057, USA
2 Department of Computational and Data Sciences, George Mason University, 4400 University Drive, Fairfax, VA 22030, Fairfax VA 22030, USA
3 Department Human Science, School of Nursing and Health Studies, Georgetown University Medical Center, 3700 Reservoir
Rd NW, Washington DC 20057, USA
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.
Keywords: K-nearest neighbor analysis, sequential analysis, personalized medicine, patients-like-me algorithm, statistical methods.
Article Information
Article History:
Received Date: 9/6/2009
Revision Received Date: 27/7/2009
Acceptance Date: 7/9/2009
Electronic publication date: 5
/11/2009
Collection year: 2009
© Alemi et al.; Licensee Bentham Open.
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (
http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
* Address correspondence to these authors at the Department of Health System Administration, School of Nursing and Health Studies, Georgetown University Medical Center, 3700 Reservoir Rd NW, Washington DC 20057, USA; Tel: 202 687 3213; Fax: 202 784 3128; E-mail: fa@georgetown.eduCorrespondence:
Department of Computational and Data Sciences, George Mason University, 4400 University Drive, Fairfax, VA 22030, Fairfax VA 22030, USA; Tel: 703.993.4511; Fax: 703.993.1491; E-mail: igriva@gmu.edu
Open Peer Review Details |
Manuscript submitted on 9-6-2009 |
Original Manuscript |
Improved Statistical Methods are Needed to Advance Personalized Medicine |