Here's the beginning to whet your appetite:
If it were not for the great variability among individuals, medicine might as well be a science and not an art.
— Sir William Osler (1892)
Over the past half century, biomedical science has developed randomized, controlled clinical-trial methods that can distinguish treatment effects from the noise of human variability. Positive results from tests of a treatment in a randomized, controlled trial provide great confidence that an intervention improves a prespecified outcome in a population defined by explicit entry criteria. These methods are rightly venerated because they have helped move medicine from anecdote to science and have largely brought about the therapeutic advances of the past 50 years. However, although population-based, randomized, controlled trials of drugs control for disease variability, they generally do not reveal why some people do not have a response to treatment, others have excessive pharmacologic responses, and still others have side effects that occur in a distinctive pattern for a given drug. Addressing this question is our next challenge.
Currently, medicine is addressing this challenge through the lens of genomic technologies. There is considerable debate about the quality, quantity, and type of evidence that would be needed to change clinical practice by introducing pharmacogenetic testing for a given drug. What methods should be used to understand individual responses once an overall population benefit has been shown in randomized, controlled trials?
The full editorial can be found here. It's a timely and important read.
