含治愈个体的复发事件下半参数比率模型

Semiparametric Rate Model for Recurrent Event Data with Cure Rate

随着科技及医疗水平的不断提高,对于一些反复发生且被认为是不可能被治愈的疾病,近年来发现有疑似治愈个体的存在.针对这一现象,本文在原来的复发事件数据的半参数比率模型基础之上,利用Logistic模型回归治愈率部分,提出一类含有有治愈个体的半参数比率模型,来刻画协变量对事件复发率的影响.同时给出该模型中未知参数的估计方法,证明这些估计的相合性和渐近性正态性.并通过数值模拟验证了这些估计在有限样本下也是有效的,并且把该模型及方法用于一组实际的膀胱癌数据分析中.

Recurrent event data usually occur in long-term studies which concern recurrence rates of the disease. In studies of medical sciences, patients who have infected with the disease, like cancer, were conventionally regarded as impossible to be cured. However, with the development of medical sciences, recently those patients were found to be possibly recovered from the disease. The recurrence rate of the events, which is of primary interest, may be affected by the cure rate that may exist. Therefore, we proposed semiparametric statistical analysis for recurrent event data with subjects possibly being cured. In our approach, we present a proportional rate model for recurrence rate with the cure rate adjusted through a Logistic regression model, and develop some estimating equations for estimation of the regression parameters, with their large sample properties, including consistency and asymptotic normality established. Numerical studies under different settings were conducted for assessing the proposed methodology and the results suggest that they work well for practical situations. The approach is applied to a bladder cancer dataset which motivated our study.