Semiparametric mixture methods for the analysis of correlated counting and survival processes with applications to study design

用于分析相关计数和生存过程的半参数混合方法及其在研究设计中的应用

• 批准号: 46119-2009
• 负责人: Dean, Charmaine
• 金额: $ 3.06万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=475054
• 关键词: Semiparametric; mixture; methods; analysis; correlated

This proposal develops efficient methods for the design of clinical studies that monitor recurrent events, for example, skin or other cancer recurrences or epileptic seizures. Sometimes continuous monitoring of individuals experiencing recurrent events is not possible or imperative. There may be situations where, ethically, patients need not be subjected to such monitoring based on the need for care, and at the same time, funds spent in continuous or overly-frequent monitoring could be better allocated; alternatively, events may occur too frequently for their exact times of occurrence to be recorded. Statistical challenges to the design of studies that monitor recurrent events lie in determining optimal times for followup and the number of followup times required, accounting for the numerous distinctions that characterize studies of human subjects, for example, substantial heterogeneity in responses to treatments amongst individuals and missingness of data. The methods employed by the proposed research afford minimal assumptions on the underlying process that generates recurrences, making them highly generalizable.

该提案开发了有效的方法来设计监测复发事件的临床研究，例如皮肤或其他癌症复发或癫痫发作。有时，对经历反复事件的个人进行连续监测是不可能或必要的。可能存在这样的情况：从伦理上讲，患者不需要根据护理需要接受此类监测，同时，可以更好地分配用于持续或过于频繁监测的资金；或者，事件可能发生得太频繁而无法记录其发生的确切时间。监测复发事件的研究设计的统计挑战在于确定最佳随访时间和所需的随访次数，并考虑到人类受试者研究的众多区别，例如个体之间对治疗反应的显着异质性和数据缺失。所提出的研究所采用的方法对产生重复的基本过程提供了最小的假设，使它们具有高度的概括性。