Effectiveness of immune-oncology chemotherapies has been presented in recent clinical trials. The Kaplan-Meier estimates of the survival functions of the immune therapy and the control often suggested the presence of the lag-time until the immune therapy began to act. It implies the use of hazard ratio under the proportional hazards assumption would not be appealing, and many alternatives have been investigated such as the restricted mean survival time. In addition to such overall summary of the treatment contrast, the lag-time is also an important feature of the treatment effect. Identical survival functions up to the lag-time implies patients who are likely to die before the lag-time would not benefit the treatment and identifying such patients would be very important. We propose the semiparametric piecewise accelerated failure time model and its inference procedure based on the semiparametric maximum likelihood method. It provides not only an overall treatment summary, but also a framework to identify patients who have less benefit from the immune-therapy in a unified way. Numerical experiments confirm that each parameter can be estimated with minimal bias. Through a real data analysis, we illustrate the evaluation of the effect of immune-oncology therapy and the characterization of covariates in which patients are unlikely to receive the benefit of treatment.

翻译：近年来，免疫肿瘤化疗的有效性已在临床试验中得到证实。免疫疗法与对照组的生存函数的Kaplan-Meier估计常提示存在滞后时间，即免疫疗法开始生效前的延迟期。这意味着在比例风险假设下使用风险比不再具有吸引力，因此研究者已探索了许多替代方案，如限制平均生存时间。除了此类治疗对比的整体性总结外，滞后时间亦是治疗效果的一个重要特征。在滞后时间之前完全相同的生存函数意味着，那些可能在滞后时间前死亡的患者无法从治疗中获益，识别此类患者至关重要。我们提出了基于半参数最大似然法的半参数分段加速失效时间模型及其推断流程。该模型不仅提供了整体治疗效果的总结，还提供了一个统一的框架来识别从免疫疗法中获益较少的患者。数值实验证实，各参数估计偏差极小。通过实际数据分析，我们展示了免疫肿瘤疗法效果的评估方法，以及对患者可能无法从治疗中获益的协变量特征的刻画。