Research methods for clinical trials in personalized medicine: A systematic review.

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.