Quality of life (QOL) end points in pharmaceutical clinical trials are at a crossroads. On the one hand, much has been learned in recent years of how to efficiently and effectively measure patient QOL. On the other hand, investigators and regulatory agencies still struggle with exactly how to assess the results of QOL end points and other patient-reported outcomes. Statisticians are often left in the position of having to bridge the gap between investigators who want to assess patient QOL and regulatory bodies who want a sound scientific rationale and analysis plan for doing so. Unfortunately, little has been written specifically for the statistical audience to assist in this translation. The purpose of this paper is to attempt to bridge this gap. We will describe the language and methods that have been successful in translating the psychometric and statistical challenges into understandable findings for investigators and regulatory agencies. One of the most important advances is the development of a general guideline for assessing clinical significance, namely the "half standard deviation" method based on the empirical rule effect size (ERES) approach. We populate the paper with concrete examples of how QOL data need not be treated any different, in terms of statistical analysis, than tumor response or other clinical end points.