Revolutionizing Patient-Reported Outcomes Analysis for Oncology Drug Development Using Population Models

Patient-reported outcomes (PRO) play a crucial role as clinical endpoint in oncology trials. Traditional statistical methods, such as hypothesis testing, have been commonly used by pharmaceutical industry and regulators to evaluate treatment efficacy on PRO endpoints. However, the analysis of PRO data remains challenging because of high variability and missing data issues. In this study, we will present examples in which inappropriate statistical analyses of PRO data can confound treatment efficacy analyses. To overcome these challenges, we propose the application of individual participant data and population models. Population models have been extensively used in pharmacokinetics and pharmacodynamics analyses and are well accepted by regulators. However, their potential in PRO data analyses, particularly in the field of oncology, remains largely untapped. This perspective article aims to highlight the value of population modeling approaches in PRO data analyses for oncology clinicians and researchers. Population models integrate individual participant data and can effectively handle the substantial variability in PRO measurements by incorporating covariates, between-subject variability, and accounting for measurement noise. By leveraging information from the population, this approach also provides accurate estimations for participants with missing data or sparse sampling. Moreover, these models could be applied to predict long-term PRO dynamics. If used appropriately, population modeling approaches could revolutionize the analysis of PRO data in oncology drug development, enabling a more comprehensive understanding of the impact of treatment on patients' lives. Our aim is to encourage stakeholders to consider population modeling as a standard and effective tool to enhance decision-making and ultimately improve patient care.