STATISTICAL METHODS IN CURRENT CANCER RESEARCH

当前癌症研究中的统计方法

• 批准号: 6131586
• 负责人: DANYU LIN
• 金额: $ 8.37万
• 依托单位: UNIVERSITY OF WASHINGTON
• 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 2000-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6131586
• 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、长期的 这项研究的目标是开发简单实用的 临床和流行病学设计和分析的统计方法 观察不完整的癌症研究。具体目标包括（1） 研究评估效果的半参数回归方法 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的成本和质量调整寿命，(2) 构建用于联合分析的非参数和半参数方法 不完整的重复测量（例如，一系列生活质量测量）和 审查失败时间（例如，癌症复发/死亡的时间） 纵向癌症研究，以及（3）探索有效的设计方法 以及两阶段生存研究的分析（例如病例队列研究、样本研究） 调查和协变量测量误差问题）。拟议的统计 模型和推理过程是基于但显着扩展 关于审查故障时间数据分析的当前知识和 不完整的重复测量。这些模型非常灵活且用途广泛 他们不需要指定任何随机的分布形式 变量或任何两个相关结果测量之间的依赖结构。 所提出的估计量和检验统计量的渐近性质将 使用计数过程鞅理论进行严格研究， 现代经验过程理论和其他概率工具。他们的经营 实际环境中的特征将通过广泛评估 计算机模拟。所提出方法的有用性将是 用真实的癌症研究来说明。研究结果将被传播 通过出版物向执业统计学家和医学研究人员提供， 讲座和软件分发。