Kim Yang-Jin, Jhun Myoungshic
Institute of Statistics, Korea University, Seoul 136-701, Korea.
Stat Med. 2008 Jan 15;27(1):3-14. doi: 10.1002/sim.2918.
In cancer trials, a significant fraction of patients can be cured, that is, the disease is completely eliminated, so that it never recurs. In general, treatments are developed to both increase the patients' chances of being cured and prolong the survival time among non-cured patients. A cure rate model represents a combination of cure fraction and survival model, and can be applied to many clinical studies over several types of cancer. In this article, the cure rate model is considered in the interval censored data composed of two time points, which include the event time of interest. Interval censored data commonly occur in the studies of diseases that often progress without symptoms, requiring clinical evaluation for detection (Encyclopedia of Biostatistics. Wiley: New York, 1998; 2090-2095). In our study, an approximate likelihood approach suggested by Goetghebeur and Ryan (Biometrics 2000; 56:1139-1144) is used to derive the likelihood in interval censored data. In addition, a frailty model is introduced to characterize the association between the cure fraction and survival model. In particular, the positive association between the cure fraction and the survival time is incorporated by imposing a common normal frailty effect. The EM algorithm is used to estimate parameters and a multiple imputation based on the profile likelihood is adopted for variance estimation. The approach is applied to the smoking cessation study in which the event of interest is a smoking relapse and several covariates including an intensive care treatment are evaluated to be effective for both the occurrence of relapse and the non-smoking duration.
在癌症试验中，相当一部分患者可以被治愈，也就是说，疾病被完全消除，不会再复发。一般来说，研发治疗方法是为了既增加患者被治愈的机会，又延长未被治愈患者的生存时间。治愈率模型代表了治愈比例和生存模型的结合，可应用于多种类型癌症的许多临床研究。在本文中，考虑了由两个时间点组成的区间删失数据中的治愈率模型，这两个时间点包括感兴趣的事件时间。区间删失数据通常出现在那些往往无症状进展、需要临床评估来检测的疾病研究中（《生物统计学百科全书》。威利出版社：纽约，1998年；2090 - 2095页）。在我们的研究中，采用了Goetghebeur和Ryan提出的近似似然方法（《生物统计学》2000年；56:1139 - 1144）来推导区间删失数据中的似然函数。此外，引入了脆弱模型来描述治愈比例和生存模型之间的关联。特别地，通过施加一个共同的正态脆弱效应，将治愈比例和生存时间之间的正相关纳入其中。使用期望最大化（EM）算法估计参数，并采用基于轮廓似然的多重填补法进行方差估计。该方法应用于戒烟研究，其中感兴趣的事件是吸烟复发，并且评估了包括重症监护治疗在内的几个协变量对复发的发生和非吸烟持续时间均有效。
