Nonlinear Mixed Effect Modeling of Patient-Reported Outcomes to Improve Treatment Effect Estimation in an Oncology Clinical Trial
- 1UNC Chapel Hill
Abstract
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.
Related
- Intra-Patient Comparison of a Minimally Invasive Lid Treatment and Traditional Blepharoplasty: Patient and Surgeon-Reported Outcomes
- Clinical Outcomes and Patient-Reported Satisfaction with Functional Orthodontic Treatment of Mandibular Condylar Process Fractures
- Patient-Reported Outcomes After Aptos Thread-Centered Treatment in Real-World Aesthetic Practice
- Prevalence, Risk Indicators, and Patient‐Reported Outcomes of Labial Gingival Recession Following Orthodontic Treatment: A Retrospective Study
- Incidence Rates of Adverse Events (CTCAE Severity ≥3) During HER2-Directed Breast Cancer Treatment Assessing the Impact of App-Based Electronic Patient-Reported Outcomes in the Randomized Controlled PRO2 Trial.
BibTeX
@misc{zhang2026nonlinear,
title = {{Nonlinear Mixed Effect Modeling of Patient-Reported Outcomes to Improve Treatment Effect Estimation in an Oncology Clinical Trial}},
author = {C. Zhang and Q. Li and G. Slim and X. Li and Y. Sun and B. Muluneh and J. Zhou},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.26.26366496},
url = {https://www.medrxiv.org/content/10.64898/2026.10.26.26366496v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Nonlinear Mixed Effect Modeling of Patient-Reported Outcomes to Improve Treatment Effect Estimation in an Oncology Clinical Trial
AU - C. Zhang
AU - Q. Li
AU - G. Slim
AU - X. Li
AU - Y. Sun
AU - B. Muluneh
AU - J. Zhou
PY - 2026
DA - 2026/10/26
PB - medRxiv
DO - 10.64898/2026.10.26.26366496
UR - https://www.medrxiv.org/content/10.64898/2026.10.26.26366496v1
AB - 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.
ER -