提出连续时间治疗效应模型，精准追踪放化疗对头颈癌患者的动态影响
CAST: Time-Varying Treatment Effects with Application to Chemotherapy and Radiotherapy on Head and Neck Squamous Cell Carcinoma
- 融合参数与非参数方法，将治疗效果建模为随时间连续变化的函数
- 在2651例头颈癌患者数据中发现治疗效应先上升后下降的动态变化规律
- 帮助临床判断疗效峰值期，适合个性化医疗决策研究者使用
因果机器学习（CML）可实现个体化治疗效应估计，优于传统相关性方法。然而，现有针对存在删失的医学生存数据的方法（如因果生存森林）仅能在固定时间点估计效应，难以捕捉随时间变化的动态特征。本文提出因果生存轨迹分析框架（CAST），将治疗效应建模为治疗后时间的连续函数。通过结合参数与非参数方法，克服离散时间点分析的局限，实现连续效应轨迹估计。以包含2,651例头颈鳞状细胞癌（HNSCC）患者的RADCURE数据集为例，CAST在人群和个体层面刻画了化疗与放疗效应随时间的变化过程。结果揭示治疗效应在随访期内先上升、达峰后下降的动态趋势，有助于临床确定治疗效益最大化的时机与人群。该框架推动了因果机器学习在头颈癌等危及生命疾病个性化治疗中的应用。源代码与数据见：https://github.com/CAST-FW/HNSCC
原文摘要 · Abstract (English)
Causal machine learning (CML) enables individualized estimation of treatment effects, offering critical advantages over traditional correlation-based methods. However, existing approaches for medical survival data with censoring such as causal survival forests estimate effects at fixed time points, limiting their ability to capture dynamic changes over time. We introduce Causal Analysis for Survival Trajectories (CAST), a novel framework that models treatment effects as continuous functions of time following treatment. By combining parametric and non-parametric methods, CAST overcomes the limitations of discrete time-point analysis to estimate continuous effect trajectories. Using the RADCURE dataset [1] of 2,651 patients with head and neck squamous cell carcinoma (HNSCC) as a clinically relevant example, CAST models how chemotherapy and radiotherapy effects evolve over time at the population and individual levels. By capturing the temporal dynamics of treatment response, CAST reveals how treatment effects rise, peak, and decline over the follow-up period, helping clinicians determine when and for whom treatment benefits are maximized. This framework advances the application of CML to personalized care in HNSCC and other life-threatening medical conditions. Source code/data available at: https://github.com/CAST-FW/HNSCC
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