Mathematical Sciences: Asymptotic Methods for Order Restricted Inference in Survival Analysis

数学科学：生存分析中阶次限制推理的渐近方法

• 批准号: 9504891
• 负责人: Richard Dykstra
• 金额: $ 3万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-07-15 至 1998-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9504891&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Asymptotic; Methods; Order

Proposal: DMS 9504891 PI: Jens Praestgaard Institution: University of Iowa Title: Asymptotic Methods for Order Restricted Inference in Survival Analysis ABSTRACT: The purpose of the research is to develop large-sample theory for inference from survival data when the unknown parameters exhibit order restrictions. Such restrictions occur frequently in survival analysis. For example, if a clinical trial involves administering a drug in increasing doses to the cohorts under study, then the survival curves computed for each group should be ordered according to dose level. In linkage analysis, for the purpose of mapping a quantitative genetic trait, it may be known that the trait under study is more prevalent for one genotype than for others. Although appropriate estimators in these settings are known, their distributional properties have not been studied. Thus, order restricted methods are often not used when it would potentially help the data analysis. For estimation purposes it would give estimators with higher degree of accuracy. For testing purposes it would give higher power to be able to consider order restricted alternatives. The research centers on investigating statistical methods in the presence of order restrictions. These restrictions occur often in biotechnological problems. For instance, in a clinical trial to determine drug efficacy, it is often the case that the drug is administered in increasing doses. In genetical linkage analysis it is often known that a certain genotype lowers the time until the onset of a disease more than do other possible genotypes. If the interest is on determining the drug efficacy (in the first case) or finding out where the gene is located (second case), then the statistical methods, which separate the noise in the data from the signal, must incorporate these known orderings. The investigators study ways of incorporating such information. They believe that the resulting statistical method ology will be more appropriate than present methods for dealing with biotechnological data where such orderings are present.

提案：DMS 9504891 PI：Jens Praestgaard机构：爱荷华州大学标题：生存分析中顺序限制推理的渐近方法 摘要： 研究的目的是发展大样本理论， 当未知参数表现出阶限制时，从生存数据推断。这种限制经常发生在生存 分析.例如，如果临床试验涉及施用 研究中的队列中增加剂量的药物，然后 各组计算的存活曲线应根据剂量水平排序。在连锁分析中，为了 通过对数量遗传性状进行作图，可以知道所研究的性状对于一种基因型比对于其他基因型更普遍。 虽然在这些环境中适当的估计 已知，但其分布特性尚未研究。 因此，顺序限制的方法通常不使用时， 可能有助于数据分析。 为了估计的目的，它会给估计更高的精度。 为了测试的目的，它将给予更高的权力，能够考虑顺序限制的替代品。 研究的重点是调查统计方法， 存在订单限制。这些限制经常发生在 生物技术问题。 例如，在确定药物功效的临床试验中，通常的情况是，药物是 给药的剂量越来越大在遗传连锁分析中 通常已知，某种基因型比其他可能的基因型更能缩短疾病发作的时间。如果 感兴趣的是确定药物功效（在第一种情况下）或找出基因的位置（第二种情况），然后 统计方法，将数据中的噪声从 信号，必须包含这些已知的排序。调查人员研究了整合这些信息的方法。他们认为 由此产生的统计方法学将更合适 比目前处理生物技术数据的方法 在存在这种排序的情况下。