Abstract
Pembrolizumab is an immune checkpoint inhibitor that has been approved for more than 20 different indications and has shown great survival benefits in various types of cancer. However, the reported benefits of pembrolizumab in patients' quality of life (QoL) have been inconsistent across different studies and different types of cancer. As oncology drug development increasingly emphasizes patient-centered care, patient-reported outcomes (PROs), particularly patient-reported QoL, are recognized as important clinical endpoints. To characterize the effects of pembrolizumab on patient-reported QoL, we conducted a model-based meta-analysis (MBMA) of published clinical trials evaluating pembrolizumab across different types of cancer. The longitudinal EORTC QLQ-C30 GHS/QOL data were extracted in our analysis as QoL scores. A population model was developed to characterize the longitudinal QoL trajectories and quantify both treatment toxicity and efficacy. Out of more than 300 screened studies, only 20 reported longitudinal EORTC QLQ-C30 QoL data. Among these, 8 studies reported no between-group differences in QoL outcomes between pembrolizumab and control arms. However, our modeling revealed that pembrolizumab was associated with greater toxicity but improved long-term QoL. Notably, our approach identified treatment effects on QoL that were not detected by traditional statistical analyses in the original publications. In summary, our study demonstrates that MBMA combined with population modeling enables more accurate evaluation of longitudinal PROs data, overcoming the limitations of conventional methods. This approach offers a robust framework for integrating patient-centered outcomes into oncology drug development and supports the broader use of PROs data in regulatory and clinical decision-making.