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

这一建议为临床研究的设计开发了有效的方法，这些研究监测复发事件，例如皮肤癌或其他癌症复发或癫痫发作。有时，对反复发作的个人进行持续监测是不可能的，也不是必须的。在某些情况下，根据护理需要，可能不需要对患者进行这种监测，同时，花费在持续监测或过于频繁监测上的资金可以得到更好的分配；或者，事件可能发生得太频繁，以至于无法记录其确切发生时间。对监测复发事件的研究设计的统计学挑战在于确定最佳的随访时间和所需的随访次数，这是人类受试者研究的许多不同之处，例如，个体对治疗的反应非常不同，数据缺失。拟议的研究采用的方法对产生递归的潜在过程提供了最小的假设，使它们具有高度的概括性。

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.