STATISTICAL METHODS IN CURRENT CANCER RESEARCH

当前癌症研究中的统计方法

• 批准号: 6377395
• 负责人: DANYU LIN
• 金额: $ 16.24万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2004-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6377395
• 关键词: computer simulation; experimental designs; health care cost /financing; health science research analysis /evaluation; human data; longitudinal human study; mathematical model; method development; model design /development; neoplasm /cancer epidemiology; quality of life; statistics /biometry

DESCRIPTION (Adapted from the Applicant's Abstract): The broad, long-term objectives of this research are the developments of simple and useful statistical methods for the design and analysis of clinical and epidemiologic cancer studies with incomplete observations. The specific aims include (1) investigation of semi-parametric regression methods for assessing the effects of covariates (e.g., cancer therapy and patient characteristics) on medical cost and quality-adjusted lifetime based on incomplete follow-up data, (2) construction of non- and semi-parametric methods for the joint analysis of incomplete repeated measures (e.g., serial quality-of-life measures) and censored failure times (e.g., times to cancer recurrence/death) from longitudinal cancer studies, and (3) exploration of efficient methods of design and analysis for two-phase survival studies (e.g., case-cohort studies, sample surveys and covariate measurement error problems). The proposed statistical models and inference procedures are built from but extend significantly the current knowledge about the analysis of censored failure time data and incomplete repeated measures. These models are highly flexible and versatile in that they do not require specifying the distributional form of any random variable or the dependence structure between any two related outcome measures. The asymptotic properties of the proposed estimators and test statistics will be investigated rigorously with the use of counting-process martingale theory, modern empirical process theory and other probability tools. Their operating characteristics in practical settings will be evaluated extensively through computer simulations. The usefulness of the proposed methods will be illustrated with real cancer studies. The research results will be disseminated to practicing statisticians and medical investigators via publications, lectures and software distributions.

描述（改编自申请人的摘要）：广泛的长期 这项研究的目标是简单有用的发展 临床和流行病学设计和分析的统计方法 癌症研究不完整。具体目的包括（1） 调查用于评估影响的半参数回归方法 医学上的协变量（例如癌症治疗和患者特征） 基于不完整的后续数据，成本和质量调整的寿命，（2） 构建非半参数方法用于联合分析 不完整的重复措施（例如，序列生活质量度量）和 审查失败时间（例如，从 纵向癌症研究和（3）探索有效设计方法 和分析两阶段生存研究（例如，病例研究，样本 调查和协变量测量误差问题）。提出的统计 建立模型和推理程序，但显着扩展了 当前有关审查的故障时间数据分析的知识和 不完整的重复措施。这些模型在高度灵活和通用 他们不需要指定任何随机的分布形式 任何两个相关结果度量之间的变量或依赖性结构。 拟议估计器和测试统计的渐近特性将 通过使用计数进程Martingale理论进行严格的调查， 现代经验过程理论和其他概率工具。他们的操作 实用环境中的特征将通过 计算机模拟。提出方法的有用性将是 用实际的癌症研究说明。研究结果将被传播 通过出版物实践统计学家和医学调查员 讲座和软件分布。