{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "UNC Chapel Hill"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "C. Zhang"
    },
    {
      "name": "Q. Li"
    },
    {
      "name": "G. Slim"
    },
    {
      "name": "X. Li"
    },
    {
      "name": "Y. Sun"
    },
    {
      "name": "B. Muluneh"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/52fnv9dw4cdkc.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.26.26366496",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.26.26366496v1.full",
  "id": "52fnv9dw4cdkc",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.26.26366496v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "oncology"
    ],
    [
      "openalex",
      "2613|Statistical Methods in Clinical Trials"
    ]
  ],
  "openalex": "W7224115133",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.26.26366496v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/52fnv9dw4cdkc.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:54Z",
      "native_id": "10.64898/2026.10.26.26366496",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.26.26366496v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Nonlinear Mixed Effect Modeling of Patient-Reported Outcomes to Improve Treatment Effect Estimation in an Oncology Clinical Trial",
  "title_text": "Nonlinear Mixed Effect Modeling of Patient-Reported Outcomes to Improve Treatment Effect Estimation in an Oncology Clinical Trial",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:57Z",
  "url": "https://research.draconitic.com/paper/52fnv9dw4cdkc",
  "venue": "medRxiv",
  "versions": []
}