The mean does not mean as much anymore: finding sub-groups for tailored therapeutics

Abstract

Background: The genomics revolution is still in its infancy, and there is much to learn about how to transform biological knowledge into useful medicines to further public health. At the bedside, we are asking how and why individual patients respond to different drug treatments in different ways. In addition to genetic mechanisms, there are many clinical markers (e.g. medical history, disease severity) as well as social/environmental factors (e.g. smoking habits) that can be used to identify who may or may not respond to treatment.

Purpose: This issue has some considerable statistical complexity, and different approaches to the analysis of clinical trials may yield more interesting insights into the problem. Novel applications of statistical methods will be discussed, and examples will be used to demonstrate sub-group identification.

Methods: In order to evaluate many potential predictors of response, we use recursive partitioning methods to identify predictor variables and their cut-off values to define sub-groups of patients with differential treatment response. Validation of this variable/model selection approach was done using independent data from other clinical trials.

Results: In one example, a classification tree was developed using baseline measures to define important sub-groups of patients that responded much better than the overall mean response in the study. In a second example, a classification tree was built based on measures of response early in treatment to predict longer-term responders and nonresponders. Limitation Classification algorithms can be prone to over-fitting, and validation of results is an important consideration. Obviously, analyses are limited by the available predictor variables.

Conclusions: Using classification trees proved to be very useful in evaluating large numbers of potential predictors to find sub-groups of patients with exceptional response. The method is easy to use, and clinicians can easily interpret and implement results. This approach can be helpful in tailoring treatments to individual patients.