肺疾病患者肺功能恢复的非线性轨迹：纵向建模方法的实证评估
Nonlinear trajectories of lung function recovery in patients with pulmonary disease: empirical evaluation of longitudinal modeling approaches
- Vanderbilt University Medical Center（范德堡大学医学中心）
- Aurum Institute（奥勒姆研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对肺疾病患者肺功能恢复的非线性轨迹，比较6种纵向建模方法，发现灵活模型可揭示传统模型遗漏的特定时期治疗差异，为建模策略选择提供依据。
AI中文摘要：
引言：肺疾病后肺功能的纵向恢复通常遵循非线性轨迹，若未能充分建模这些轨迹，可能会导致治疗效应的估计出现偏差或误导。然而，一个重要的方法学缺口仍然存在，因为针对非线性肺功能轨迹建模的统计方法评估有限。方法：我们使用一项肺结核（TB）2期随机试验的数据，比较了几种用于表征预测1秒用力呼气容积百分比（FEV1p）恢复情况的纵向建模方法。我们估计了在180天随访期间，各治疗组与对照组之间重复测量的平均FEV1p的差异。我们比较了6种不同的统计模型：（1）线性混合效应模型、（2）分段线性混合效应模型、（3）二次混合效应模型、（4）自然三次样条混合效应模型、（5）带有指数恢复函数的非线性混合效应模型、（6）广义加性混合模型。我们讨论了这些方法的假设、临床解释以及得出的治疗效应估计值。结果：肺结核试验分析的结果显示，传统线性混合效应模型在随访期间提供的治疗差异证据有限，而几种灵活模型则识别出恢复特定时期的显著差异。结论：灵活的纵向模型可作为传统线性混合效应模型的补充，揭示随访特定时期的治疗差异，而这些差异可能因假设单一线性趋势而被掩盖。非线性建模策略的选择应基于科学目标、可用数据以及临床可解释性与灵活性之间期望的平衡。
英文摘要：
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.
