癌症放射治疗的目标是向肿瘤提供规定的放射剂量，同时最大限度地减少周围健康组织的剂量。为了评估治疗计划，健康器官的剂量分布通常总结为剂量体积直方图 (DVH)。正常组织并发症概率 (NTCP) 建模的重点是利用从 DVH 中提取的特征进行患者级别的风险预测，但很少有人考虑采用因果框架来评估替代治疗计划的安全性。我们基于确定性和随机干预提出了 NTCP 的因果估计量，并提出了基于边际结构模型的估计量，这些模型在剂量、体积和毒性风险之间施加了双变量单调性。通过模拟研究这些估计器的特性，并在肛管癌患者的放射治疗背景下说明它们的使用。© 作者 2024。由牛津大学出版社出版。

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.© The Author(s) 2024. Published by Oxford University Press.