A NONPARAMETRIC MLE SURVIVAL ANALYSIS MODULE

经审查和截断的数据经常来自与艾滋病毒/艾滋病有关的数据以及其他临床试验和观察性研究。需要先进的非参数生存分析技术来处理这些复杂的不完全数据，而不牺牲建模原则。该项目根据生存分析的最新进展开发了一个可用的软件模块，这些模块通常适用于这些不完整的数据。该软件模块包括以下创新的估计技术：(1)区间删失、双删失和截尾数据下生存函数的非参数极大似然估计(NPMLE)；(2)区间删失和双删失数据下比例风险模型的最大轮廓似然方法；以及(3)在现代统计计算环境中的实现。该软件模块通过以下推理过程来补充其估计技术：(1)基于非参数Bootstrap、基于半参数似然比的置信度区间和频带；(2)基于Profile似然的比例风险模型中的Rao、Wald和似然比检验和置信集。该项目的可行性基于几个基础，其中一些在第一阶段的研究中得到了巩固和扩展：(1)计算NPMLE的显著更快的混合算法；(2)计算最大轮廓似然估计的有效最大化技术；(3)面向对象的数据分析和图形软件环境S-Plus来托管这些技术。拟议的商业应用：拟议的产品将在广泛的生物医学和公共卫生研究人员和从业者中有现成的市场，因为它提供了其他生存分析产品中没有的高级功能，并解决了常规应用程序中经常遇到的非传统不完整数据。

Censored and truncated data frequently arise from HIV/AIDS related and other clinical trials and observational studies. Advanced nonparametric survival analysis techniques are required to handle these complicated incomplete data without sacrificing modeling principles. This project develops a usable software module based on recent advances in survival analysis that are routinely applicable to these incomplete data. The software module includes the following innovative estimation techniques: (1) nonparametric maximum likelihood estimator (NPMLE) for survival functions with interval censored, doubly censored and truncated data; (2) maximum profile likelihood approach to the proportional hazard model with interval censored and doubly censored data; and (3) implementation in a modern statistical computing environment. The software module complements its estimation techniques with the following inference procedures: (1) Nonparametric bootstrap, semiparametric likelihood ratio based confidence intervals and bands, (2) Rao, Wald and likelihood ratio tests and confidence sets in the proportional hazards model by profile likelihood. The feasibility of the project rests on several foundations, some of which consolidated and extended in the Phase I research: (1) a significantly faster hybrid algorithm for computing the NPMLE; (2) an effective maximization technique for computing the maximum profile likelihood estimates; (3) an object- oriented data analysis and graphics software environment S-PLUS to host these techniques. PROPOSED COMMERCIAL APPLICATIONS: The proposed product will have a ready market in extensive biomedical and public health researchers and practitioners because it provides advanced functionalities that are not found in other survival analysis products, and addresses non-traditional incomplete data commonly encountered in routine applications.