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

该提案为监测复发性事件（例如皮肤或其他癌症复发或癫痫发作）的临床研究设计开发了有效的方法。有时，对经历复发性事件的个体进行持续监测是不可能或不必要的。在某些情况下，从伦理上讲，基于护理需要，患者不需要接受此类监测，同时，用于持续或过于频繁监测的资金可以得到更好的分配;或者，事件可能发生得过于频繁，无法记录确切的发生时间。监测复发性事件的研究设计的统计学挑战在于确定随访的最佳时间和所需的随访次数，考虑到人类受试者研究的许多特征，例如，个体之间对治疗的反应存在显著异质性和数据缺失。所提出的研究方法提供了最小的假设，产生递归的基本过程，使他们高度概括。

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.