Semiparametric Piecewise Accelerated Failure Time Model for the Analysis of Immune-Oncology Clinical Trials
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用于肿瘤免疫治疗临床试验的半参数分段加速失效时间模型
- 研究问题
- 面对免疫治疗起效滞后及非比例风险，如何同时概括总体治疗差异、估计滞后特征，并识别可能较少受益的患者？
- 方法与思路
- 作者提出半参数分段加速失效时间模型，采用半参数最大似然推断，并以数值实验和真实数据分析展示其应用。
- 材料陈述的贡献
- 摘要称模型在统一框架下提供总体治疗摘要和低受益患者识别，数值实验显示参数估计偏差较小。
- 阅读关注点
- 应核对分段点与滞后时间的关系、删失处理、模型可识别性，以及患者受益判断如何由生存模型和协变量导出。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明删失与识别条件，也未给出偏差大小或患者分类验证结果。
原文摘要
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.