Constructing estimands for overall survival within causal frame work for dynamic treatment schemes in oncology: Simulation and a clinical trial example

Overall survival (OS) is an important endpoint in regulatory approval and health technology assessment (HTA). Using current statistical methods, overall survival rates for patients given new therapies in clinical trials may be overestimated if a non-trivial proportion of the patients receive treatment changes after the trial. Adjustments need to be made to cancer trials to take into account the availability of new treatments which may skew the survival rates.
Some statistical methods have been proposed in recent research to adjust the biased overall survival rate of clinical trial participants, but there are issues with these methods as, for example, although some disease-related patient characteristics were considered in these methods, they fail to recognize that the patient characteristics could change over time. Moreover, the current statistical methods assume that all the treatments a patient may receive for the disease after a clinical trial will have the same effect on that disease, whereas a new innovative treatment may have a much greater impact.
Hence, in this project we are introducing a new statistical method to estimate overall survival for patients who receive a subsequent anticancer treatment, where the bias can be controlled. Our aim is to develop a new method for analyzing clinical trial data to give more realistic overall survival rates.

总生存期（Overall Survival, OS）是监管审批与卫生技术评估（Health Technology Assessment, HTA）中的重要终点指标。采用现有统计方法开展分析时，若临床试验中存在相当比例的患者在试验结束后变更治疗方案，则接受新疗法的临床试验患者的总生存期率可能被高估。对此需对癌症临床试验进行调整，以纳入可能扭曲生存期评估结果的新型治疗可及性因素。
近年已有多项研究提出统计方法，用于校正临床试验参与者总生存期的评估偏倚，但此类方法仍存在诸多局限。例如，尽管部分方法纳入了疾病相关的患者特征，却未考虑患者特征可能随时间发生动态变化。此外，现有统计方法假设患者在临床试验后接受的所有针对该疾病的治疗对疾病具有同等疗效，而新型创新疗法往往具备更为显著的治疗效果。
有鉴于此，本项目提出一种全新的统计方法，用于估算后续接受抗癌治疗的患者总生存期，该方法可实现偏倚可控。本研究旨在开发一款可用于分析临床试验数据的新型方法，以获得更贴合临床实际的总生存期率。