STATISTICAL METHODS IN CURRENT CANCER RESEARCH

描述（改编自申请人的摘要）： 本研究的目的是开发简单实用的 临床和流行病学设计和分析的统计方法 癌症研究，观察不完整。具体目标包括：（1） 评估影响的半参数回归方法的研究 协变量（例如，癌症治疗和患者特征） 基于不完整随访数据的成本和质量调整寿命，（2） 非参数和半参数方法的构造 不完整的重复测量（例如，系列生活质量测量）和 删失的故障时间（例如，至癌症复发/死亡的时间） 纵向癌症研究，（3）探索有效的设计方法 以及用于两阶段存活研究的分析（例如，病例队列研究，样本 调查和协变量测量误差问题）。拟定的统计 模型和推理程序是建立在，但大大扩展了 当前关于截尾失效时间数据分析的知识， 不完整的重复措施。这些型号具有高度灵活性和通用性， 它们不需要指定任何随机变量的分布形式， 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 用计数过程鞅理论进行严格的研究， 现代经验过程理论和其他概率工具。其操作 将通过以下方式广泛评估实际环境中的特性： 计算机模拟所提出的方法的实用性将是 用真实的癌症研究来说明。研究成果将在 通过出版物提供给执业统计学家和医学研究人员， 讲座和软件分发。

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.