The Open Translational Medicine Journal

2009, 1 : 16-20
Published online 2009 November 5 . DOI: 10.2174/1876399500901010016
Publisher ID: TOTRANSMJ-1-16

RESEARCH ARTICLE
 Improved Statistical Methods are Needed to Advance Personalized Medicine

Farrokh Alemi, *,1 , Harold Erdman1 , Igor Griva, * ,2 and 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

* 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.edu

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.