Using the probability-probability plot and index to augment interpretation of treatment effect for patient-reported outcome measures

Abstract

The interpretation of treatment effect can pose challenges, especially for patient-reported outcomes. As subjective assessments, patient-reported outcomes frequently lack a historical record to support what their scores mean, making their interpretation of treatment differences challenging. We show how the probability-probability (p-p) plot a graph of the test-treatment distribution percentiles versus the control-treatment distribution percentiles, can complement and supplement interpretation of treatment effect. From this plot, we introduce the p-p index as a new measure of treatment effect, illustrating the method with two examples. The p-p index represents, across all percentiles, the average difference in percentile rank for any pair of subjects on two different treatments with the same outcome score. This measure, which complements other metrics of treatment effect, captures full information by integrating across all percentiles and thus accurately summarizes and augments the interpretation of treatment effect.