论文标题
用于治疗速率模型的简化随机EM算法具有负二项式竞争风险：乳腺癌数据的应用
A Simplified Stochastic EM Algorithm for Cure Rate Model with Negative Binomial Competing Risks: An Application to Breast Cancer Data
论文作者
论文摘要
在本文中，考虑了竞争风险下的长期生存模型。假定未观察到的竞争风险遵循负二项式分布，可以捕获过度分散和不足。考虑到潜在的竞争风险是缺失的数据，开发了众所周知的期望最大化（EM）算法的变化，称为随机EM算法（SEM）。结果表明，SEM算法避免计算复杂的期望，这是SEM算法比EM算法的主要优势。提出的过程还允许将目标函数分为两个简单的函数，一个函数与与治疗速率关联的参数相对应，另一个对应于与进度时间相关的参数。这种方法的优点是，每个具有较低参数维度的简单函数都可以独立最大化。进行了广泛的蒙特卡洛模拟研究，以比较SEM和EM算法的性能。最后，分析了乳腺癌的存活数据，并表明SEM算法的性能优于EM算法。
In this paper, a long-term survival model under competing risks is considered. The unobserved number of competing risks is assumed to follow a negative binomial distribution that can capture both over- and under-dispersion. Considering the latent competing risks as missing data, a variation of the well-known expectation maximization (EM) algorithm, called the stochastic EM algorithm (SEM), is developed. It is shown that the SEM algorithm avoids calculation of complicated expectations, which is a major advantage of the SEM algorithm over the EM algorithm. The proposed procedure also allows the objective function to be split into two simpler functions, one corresponding to the parameters associated with the cure rate and the other corresponding to the parameters associated with the progression times. The advantage of this approach is that each simple function, with lower parameter dimension, can be maximized independently. An extensive Monte Carlo simulation study is carried out to compare the performances of the SEM and EM algorithms. Finally, a breast cancer survival data is analyzed and it is shown that the SEM algorithm performs better than the EM algorithm.
