Background: Personalized medicine, the notion that an individual’s genetic and other characteristics can be used to individualize the diagnosis, treatment and prevention of disease, is an active and exciting area of research, with tremendous potential to improve the health of society.
Methods: Seventy-six studies using personalized medicine analysis techniques published from 2006 to 2010 in six high-impact journals - Journal of the American Medical Association, Journal of the National Cancer Institute, Lancet, Nature, Nature Medicine, and the New England Journal of Medicine - were reviewed. Selected articles were manually selected based on reporting of the use of genetic information to stratify subjects and on analyses of the association between biomarkers and patient clinical outcomes.
Results: We found considerable variability and limited consensus in approaches. Approaches could largely be classified as data-driven, seeking discovery through statistical analysis of data, or knowledge-driven, relying heavily on prior biological information. Some studies took a hybrid approach. Eliminating two articles that were retracted after publication, 56 of the remaining 74 (76%) were cancer-related.
Conclusions: Much work is needed to standardize and improve statistical methods for finding biomarkers, validating results, and efficiently optimizing better individual treatment strategies. Several promising new analytic approaches are available and should be considered in future studies of personalized medicine.
Ren, Zheng; Davidian, Marie; George, Stephen L.; Goldberg, Richard M.; Wright, Fred A.; Tsiatis, Anastasios A.; and Kosorok, Michael R., "Research Methods for Clinical Trials in Personalized Medicine: A Systematic Review" (February 2012). The University of North Carolina at Chapel Hill Department of Biostatistics Technical Report Series. Working Paper 25.