Sieve based full likelihood approach for the Cox proportional hazards model with applications to immunotherapies trials

在临床研究中，至事件发生时间结局被广泛用于评价新治疗 功效当完全随机化不可能或不成功时， 风险模型可用于调整混杂效应，并增加统计 动力. II期研究通常数量相对较少，因此生存分析结果为 不太可靠或没有足够的功率来检测实验的阳性结果 治疗该项目旨在提供更好的生存分析结果相比，现有 小样本研究方法。筛分法已成为一种流行的方法， 生存分析，我们建议使用筛极大似然估计，以改善 生存分析结果，并提供相应的计算工具， 公共使用。

In clinical studies, the time-to-event outcomes are widely used to evaluate a new treatment efficacy. When complete randomization is not possible or successful the Cox proportional hazards model can be used to adjust for the confounding effects and to increase statistical power. Phase II studies usually have relatively small number, so the survival analysis results are not very reliable or do not have enough power to detect a positive result of the experimental treatment. This project aims to provide better survival analysis results compared to existing methods for small sample studies. Sieve method has becoming a popular approach in survival analysis, we propose to use the sieve maximum likelihood estimations to improve survival analysis results for small studies and provide corresponding computing tools for public use.