An Application of the Patient Rule-Induction Method to Detect Clinically Meaningful Subgroups from Failed Phase III...

Phase III superiority clinical trials have negative results (new treatment is not statistically better than standard of care) due to a number of factors, including patient and disease heterogeneity. However, even a treatment regime that fails to show population-level clinical improvement will have a subgroup of patients that attain a measurable clinical benefit.

The goal of this paper is to modify the Patient Rule-Induction Method to identify statistically significant subgroups, defined by clinical and/or demographic factors, of the clinical trial population where the experimental treatment performs better than the standard of care and better than observed in the entire clinical trial sample.

We illustrate this method using part A of the SUCCESS clinical trial, which showed no overall difference between treatment arms: HR (95% CI) = 0.97 (0.78, 1.20). Using PRIM, we identified one subgroup defined by the mutational profile in BRCA1 which resulted in a significant benefit for adding Gemcitabine to the standard treatment: HR (95% CI) = 0.59 (0.40, 0.87).

This result demonstrates that useful information can be extracted from existing databases that could provide insight into why a phase III trial failed and assist in the design of future clinical trials involving the experimental treatment.