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Nonlinear trajectories of lung function recovery in patients with pulmonary disease: empirical evaluation of longitudinal modeling approaches — arXiv:2608.30015

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.30015
License (as archived): CC0-1.0
Archived: 2026-10-17T09:02:22.877Z
SHA-256 of the archived text: 484e09f7e3f0d630da8c77acac3f5b466a9c6bd5acc8bb60d4042e6526e14975
Stable URL of this page: https://metraipolis.com/documento/dc_293bc49ee5bb

How to cite

Nonlinear trajectories of lung function recovery in patients with pulmonary disease: empirical evaluation of longitudinal modeling approaches — arXiv:2608.30015 — arXiv (en), https://arxiv.org/abs/2608.30015 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_293bc49ee5bb (Metraipolis archive dc_293bc49ee5bb, sha256 484e09f7e3f0d630da8c77acac3f5b466a9c6bd5acc8bb60d4042e6526e14975)

BibTeX

@misc{dc293bc49ee5bb,
  title = {Nonlinear trajectories of lung function recovery in patients with pulmonary disease: empirical evaluation of longitudinal modeling approaches — arXiv:2608.30015},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.30015},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_293bc49ee5bb; sha256 484e09f7e3f0d630da8c77acac3f5b466a9c6bd5acc8bb60d4042e6526e14975},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30015. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Introduction: Longitudinal lung function recovery after pulmonary disease commonly follows nonlinear trajectories, and failure to adequately model these trajectories can lead to biased or misleading estimates of treatment effects. However, an important methodological gap remains as there is limited assessment of statistical methods for modeling nonlinear lung function trajectories. Methods: We compared several longitudinal modeling approaches for characterizing recovery in percent predicted forced expiratory volume in one second (FEV1p) using data from a phase 2 randomized trial for pulmonary tuberculosis (TB). We estimate the differences in repeated mean FEV1p between each treatment arm and control arm over a 180-days follow-up period. We compared 6 different statistical models: (1) linear mixed-effects model, (2) a piecewise linear mixed-effects model, (3) quadratic and (4) natural cubic spline mixed-effects models, (5) a nonlinear mixed-effects model with exponential recovery function, and (6) a generalized additive mixed model. We discussed the assumptions, clinical interpretations, and resulting treatment-effect estimates across these approaches. Results: The results from the TB trial analyses showed that the conventional linear mixed-effects model provided limited evidence of treatment differences over follow-up, whereas several flexible models identified significant differences during specific periods of recovery. Conclusion: Flexible longitudinal models can complement conventional linear mixed-effects models by revealing treatment differences at certain periods of follow-up that may be obscured by assuming a single linear trend. The choice of nonlinear modeling strategy should be guided by the scientific objective, available data, and the desired balance between clinical interpretability and flexibility.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in stat.AP).
Categorie arXiv: Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2608.30015 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T08:39:48.478Z dalla pagina https://arxiv.org/catchup/stat.AP/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).