如何科学地衡量某种处理(如新的治疗方法)的效应是临床试验以及其它统计应用中的首要问题。传统的统计分析方法通常衡量处理的相对于整个样本而言的平均效应。然而，在临床试验中通常发现处理对某些个体是有效的，而对其他个体的效用是不明显的，甚至是负面的。按照处理的效应的大小不同将把整个样本分为若干组；每一组被称为一个子群。因此，如何识别子群尤为重要。另一方面，作为响应变量，临床试验中个体的生存时间通常会发生删失；这使得子群的识别更具挑战性。生存分析中子群问题的统计分析方法亟待建立与发展。本项目创造性地从惩罚函数、潜在变量、门限作用以及交互作用四个不同的角度对生存分析中子群问题深入地进行研究，采用和发展现代统计理论与技巧，系统地建立子群的识别与评估的统计方法，科学而全面地刻画在每个子群中处理对生存时间的影响方式，从而为临床试验者提供理论基础与方法指导。

How to confirm and quantify the treatment (e.g., new therapy) effect is often the primary objective of a randomized clinical trial and otherwise statistical applications. Sophisticated statistical methods yield the final result (positive or negative) of the trial that is a conclusion with respect to the average treatment effect on the entire study population. However, it is frequently encountered that a treatment may be quite effective for some subset of a population, but mildly effective, ineffective, or even harmful for others. Different magnitudes of treatment effects among the entire population would eventually classify it into several pieces; each piece is called a subgroup. Its identification is of critical importance. On the other hand, as responses of interest, the right censored survival times of some subjects make the identification challengeable in the clinical trial. It is eager to develop statistical methods tailored to the subgroup analysis in survival analysis (SASA). Novelly taking different views respectively from the penalty function, the latent variable, the threshold effect, and the interaction effect, this proposal will offer deep insight into SASA. It aims to systematically establish the identification and evaluation methods for subgroups by employing and developing modern statistical theory and techniques, and comprehensively describe the effect mechanisms of treatment on the survival times within each subgroup. Remarkably, the output of this proposal will afford investigators the solid theoretical basis and methodological guidance, based on which the clinical conclusions can be scientifically achieved.