Statistical Methods in Current Cancer Research

描述（由申请人提供）：本研究的广泛、长期目标是开发统计方法，用于分析纵向癌症研究的删失失效时间和不完整重复测量。这一竞争性更新应用的具体目标包括：（1）研究基于有效似然的方法，用于在右截尾或区间截尾下可能相关的故障时间的半参数回归模型;（2）构建基于秩的推理程序，用于不完全重复测量的回归分析;（3）发展统计学方法，用于基于删失的发病年龄表型或数量性状的不完全重复测量来检测遗传连锁，并用于评估连锁分析中全基因组的统计学显著性;（4）在删失失效时间表型关联分析中，探索适应群体分层的策略。其中一些主题是长期存在的统计问题，而另一些则是当前癌症研究中出现的问题。拟议的解决办法是建立在健全的统计原则之上的。新的估计量和检验统计量的渐近性质将严格研究使用计数过程鞅理论，现代经验过程理论和半参数有效性理论。它们在实际环境中的操作特性将通过计算机模拟进行广泛评估。建议的推理程序的有用性将说明与真实的医学研究，其中一些是在北卡罗来纳州大学进行的。将开发用于实施新方法的软件供公众使用。这项研究不仅将推进纵向数据分析、生存分析和统计遗传学领域，还将为癌症研究人员提供有价值的新工具。

DESCRIPTION (provided by applicant): The broad, long-term objectives of this research are the developments of statistical methods for the analysis of censored failure times and incomplete repeated measures from longitudinal cancer studies. The specific aims of this competing renewal application include: (1) investigation of efficient likelihood-based methods for semi-parametric regression models for possibly correlated failure times subject to right or interval censoring; (2) construction of rank-based inference procedures for the regression analysis of incomplete repeated measures; (3) development of statistical methods for detecting genetic linkage based on censored age-of onset phenotypes or incomplete repeated measures of a quantitative trait and for assessing genome-wide statistical significance in linkage analysis; (4) exploration of strategies for accommodating population stratification in the association analysis of censored failure time phenotypes. Some of these topics are longstanding statistical problems while others are emerging issues from current cancer studies. The proposed solutions are built on sound statistical principles. The asymptotic properties of the new estimators and test statistics will be studied rigorously with the use of counting-process martingale theory, modern empirical process theory and semi-parametric efficiency theory. Their operating characteristics in practical settings will be evaluated extensively through computer simulation. The usefulness of the proposed inference procedures will be illustrated with real medical studies, some of which are carried out at the University of North Carolina. The software implementing the new methodologies will be developed for public use. This research will not only advance the fields of longitudinal data analysis, survival analysis and statistical genetics, but will also provide valuable new tools to cancer researchers.