Statistical Methods in Cancer Research

在癌症研究中的统计方法

• 批准号: 7909203
• 负责人: DANYU LIN
• 金额: $ 23.65万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7909203
• 关键词: Address; Algorithms; Applied Research; Cations; Classification; Clinical; Complex; Computer Simulation; Data; Disease; Disease Association; Environmental Risk Factor; Epidemiology; Failure; Family; Family Study; General Population; Genetic; Genotype; Grant; Haplotypes; Hazard Models; Individual; Investigation; Joints; Malignant Neoplasms; Methods; Modeling; North Carolina; Performance; Population; Principal Investigator; Procedures; Process; Property; Research; Research Personnel; Sampling; Solutions; Statistical Data Interpretation; Statistical Methods; Stratification; Structure; Survival Analysis; Techniques; Testing; Time; Universities; Work; anticancer research; base; design; experience; genetic association; public health relevance; research and development; response; simulation; sound; statistics; theories; tool; trait; user friendly software

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. PUBLIC HEALTH RELEVANCE: 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.

描述（由申请人提供）：这项研究的广泛的长期目标是统计方法的发展和分析临床和流行病学癌症研究，具有或没有遗传成分。此竞争更新应用的具体目的包括：（1）探索单变量和多变量连续响应变量的半参数线性转换模型，（2）开发图形和数值技术，以评估模型的适当性和预测性的准确性，并在正确的指定失败时间数据（3）研究中的半参数转换模型（3）研究模型（3） 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缺少值）在分析单倍型 - 疾病关联的分析中，以及（7）在无关个体的遗传关联研究中解决种群分层问题。所有这些问题都是由主要研究者的应用研究经验激发的，并且与当前的癌症研究高度相关。所提出的解决方案基于可能性和其他合理的统计原理。新估计量和测试统计数据的大样本特性将通过现代经验过程理论和半参数效率理论来严格建立。将开发高效且可靠的数值算法来实施推理过程。提出的方法将通过计算机模拟进行广泛的评估，并应用于大量癌症研究，其中大多数是在北卡罗来纳大学进行的。用户友好的软件将免费提供给公众。这项研究不仅将显着提高生存分析，纵向数据分析和统计遗传学的领域，而且还将为癌症研究人员提供有价值的新工具。 公共卫生相关性：这项研究的广泛，长期目标是用于设计和分析临床和流行病学癌症研究的统计方法的发展，有或没有遗传成分。