Statistical Methods in Current Cancer Research

描述（由申请人提供）：本研究的广泛、长期目标是发展用于分析纵向癌症研究中审查失败时间和不完整重复测量的统计方法。这一竞争性更新应用的具体目标包括：(1)研究基于似然的半参数回归模型的有效方法，该模型适用于可能相关的故障时间，并受到正确或间隔审查；(2)构建基于秩的不完全重复测度回归分析推理程序；(3)开发基于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的统计方法，并在连锁分析中评估全基因组的统计显著性；(4)在截尾失效时间表型的关联分析中，探索适应种群分层的策略。其中一些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新问题。提出的解决方案是建立在可靠的统计原则之上的。利用计数过程鞅理论、现代经验过程理论和半参数效率理论，对新估计量和检验统计量的渐近性质进行了严格的研究。它们在实际环境中的工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通过实际的医学研究加以说明，其中一些研究是在北卡罗来纳大学进行的。将开发执行新方法的软件供公众使用。这项研究不仅将推动纵向数据分析、生存分析和统计遗传学领域的发展，而且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.