Nonlinear Mixed Effect Modeling of Patient-Reported Outcomes to Improve Treatment Effect Estimation in an Oncology Clinical Trial

Background: Patient-reported outcomes (PROs) capture how treatments affect patient symptoms, functioning, and quality of life, but substantial variability can obscure treatment effects. We evaluated whether NLME modeling could improve treatment-effect estimation for PRO endpoints by accounting for data variability. Methods: We retrospectively analyzed participant-level data from the phase III coBRIM trial ( NCT01689519 ), comparing cobimetinib plus vemurafenib with placebo plus vemurafenib in melanoma. EORTC QLQ-C30 global health status/quality of life (GHS/QoL) scores were evaluated using observed-score comparisons, mixed models for repeated measures (MMRM), and NLME modeling. The NLME framework, informed by pharmacology knowledge and context, characterized longitudinal GHS/QoL trajectories while quantifying between- and within-patient variability. Model-estimated scores and trajectory parameters were compared between treatments. Results: A total of 202 participants with 1,443 GHS/QoL assessments were analyzed. Observed GHS/QoL scores were highly variable, and neither observed-score comparisons nor MMRM identified statistically significant treatment differences at Cycle 8 Day 1. NLME modeling identified significantly less deterioration in longitudinal GHS/QoL with cobimetinib plus vemurafenib (P = 0.036). Model-estimated GHS/QoL scores were also significantly higher with combination treatment at Cycle 8 Day 1 (P = 0.049). Model simulations estimated a 10.4-point between-group difference (95% interval, −6.2 to 26.9), exceeding a commonly used threshold for clinical importance, although uncertainty remained. Conclusions: NLME modeling can improve PRO data analysis by accounting for substantial between- and within-patient variability and incorporating pharmacological principles to characterize disease- and treatment-related PRO changes over time. This approach may improve treatment-effect estimation and reveal clinically meaningful differences overlooked by conventional analyses.