Abstract
With the rapid advancement of scientific understanding in the medical field, everyday use of personalized medicine appears within our grasp. Unfortunately, there are still challenges to overcome. In many therapeutic areas, there remains a lack of deep understanding of disease processes and treatments. Additionally, drug development proceeds over a 5–10 year timeframe, during which time knowledge of treatments and potential predictive biomarkers continues to evolve. For successful development of a drug that is tailored to a biomarker-defined patient population and approved by regulators for such use, employment of appropriate statistical design and analysis methods is paramount. Consequently, statisticians can play a leading role in transforming the practice of medicine to a more personalized approach. We describe four perspectives in clinical development, together with examples of each, and discuss how to approach the problem of demonstrating that a treatment works better in a biomarker-defined subgroup of patients than in its complementary subgroup. The four perspectives provide a framework for design of clinical trials and subsequent analyses as they relate to clinical development. Subgroup identification is described as a controlled, disciplined search for finding the right patient for treatment and is distinguished from traditional, exploratory subgroup analysis.
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