A Model-Based Meta-Analysis of Pembrolizumab Effects on Patient-Reported Quality of Life: Advancing Patient-Centered Oncology Drug Development
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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 nonlinear mixed effect (NLME) model was developed to characterize the longitudinal QoL trajectories over time 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 NLME 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.
Study Highlights
What is the current knowledge on the topic?
Patient-reported outcomes (PROs), particularly patient-reported quality of life (QoL) measures, are essential for patient-centered oncology care. However, PROs data are often underutilized in clinical decision-making due to their subjective nature, high variability, and limited alignment with objective clinical endpoints. Pembrolizumab, a PD-1 immune checkpoint inhibitor, has demonstrated significant survival benefits across multiple cancer types. Yet, many clinical trials have reported no significant improvements in QoL with pembrolizumab treatment.
What question did this study address?
This study addressed the questions whether pembrolizumab improves patient-reported QoL and whether population nonlinear mixed-effects (NLME) model can uncover treatment benefits on QoL that may be missed by traditional statistical approaches.
What this study adds to our knowledge?
This model-based meta-analysis study shows that NLME modeling approach effectively characterizes treatment effects of pembrolizumab on patient-reported QoL by leveraging longitudinal data. It also highlights the limitations of conventional analyses and the urgent need for standardized PRO instruments and reporting practices.
How this might change clinical pharmacology and therapeutics?
This study supports integrating NLME modeling into PROs data analysis to enhance the detection of treatment effects, thereby promoting patient-centered decision-making in oncology drug development and informing future regulatory evaluations.
