Statistical Methods in Cancer Research

描述(由申请人提供)：这项研究的广泛、长期目标是为设计和分析临床和流行病学癌症研究的统计方法的发展，无论是否有遗传成分。这种竞争性更新应用的具体目标包括：(1)探索单变量和多变量连续响应变量的半参数线性变换模型，(2)开发图形和数值技术来评估右删失故障时间数据的半参数变换模型下的模型充分性和预测准确性，(3)研究用于分析经历区间删失的单变量和多变量故障时间数据的半参数变换模型，(4)寻求用于分析具有右删失数据的加速故障时间和加速危险模型的统计上有效和计算上可行的程序，(5)研究用于家族性研究中复杂疾病特征的联合链接和关联分析的方差分量模型，(6)在单倍型-疾病关联分析中处理复杂的数据结构(例如，家庭数据、选择性基因分型以及具有缺失值的相关遗传和环境因素)，以及(7)解决无关个体遗传关联研究中的群体分层问题。所有这些问题都是由首席研究员的应用研究经验推动的，与当前的癌症研究高度相关。提出的解决方案是基于似然性和其他合理的统计原理。新估计量和检验统计量的大样本性质将通过现代经验过程理论和半参数有效性理论严格建立。将开发高效和可靠的数值算法来实现推理过程。建议的方法将通过计算机模拟进行广泛评估，并应用于大量癌症研究，其中大部分研究是在北卡罗来纳大学进行的。方便使用的软件将向公众免费提供。这项研究不仅将显著推进生存分析、纵向数据分析和统计遗传学领域的发展，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 designs and analysis of clinical and epidemiological cancer studies, with or without genetic components. The specific aims of this competing renewal application include: (1) exploring semiparametric linear transformation models for univariate and multivariate continuous response variables, (2) developing graphical and numerical techniques to assess model adequacy and predictive accuracy under semi- parametric transformation models for right censored failure time data, (3) studying semiparametric transformation models for the analysis of univariate and multivariate failure time data subject to interval censoring, (4) pursuing statistically efficient and computationally feasible procedures for the analysis of accelerated failure time and accelerated hazards models with right censored data, (5) investigating variance-components models for the joint linkage and association analysis of complex disease traits in family studies, (6) handling complex data structures (e.g., family data, selective genotyping, and correlated genetic and environmental factors with missing values) in the analysis of haplotype-disease associations, and (7) addressing the issue of population stratification in genetic association studies of unrelated individuals. All these problems are motivated by the principal investigator's applied research experiences and are highly relevant to current cancer research. The proposed solutions are based on likelihood and other sound statistical principles. The large-sample properties of the new estimators and test statistics will be established rigorously via modern empirical process theory and semiparametric efficiency theory. Efficient and reliable numerical algorithms will be developed to implement the inference procedures. The proposed methods will be evaluated extensively through computer simulation and be applied to a large number of cancer studies, most of which are carried out at the University of North Carolina. User-friendly software will be freely available to the general public. This research will not only significantly advance the fields of survival analysis, longitudinal data analysis and statistical genetics, but will also provide valuable new tools to cancer researchers.