Balakrishnan Narayanaswamy, Pal Suvra
Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada Department of Statistics, King Abdulaziz University, Jeddah, Saudi Arabia
Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada.
Stat Methods Med Res. 2016 Aug;25(4):1535-63. doi: 10.1177/0962280213491641. Epub 2013 Jun 5.
Recently, a flexible cure rate survival model has been developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell-Poisson distribution. This model includes some of the well-known cure rate models discussed in the literature as special cases. Data obtained from cancer clinical trials are often right censored and expectation maximization algorithm can be used in this case to efficiently estimate the model parameters based on right censored data. In this paper, we consider the competing cause scenario and assuming the time-to-event to follow the Weibull distribution, we derive the necessary steps of the expectation maximization algorithm for estimating the parameters of different cure rate survival models. The standard errors of the maximum likelihood estimates are obtained by inverting the observed information matrix. The method of inference developed here is examined by means of an extensive Monte Carlo simulation study. Finally, we illustrate the proposed methodology with a real data on cancer recurrence.
最近，通过假设感兴趣事件的竞争原因数量遵循康威-麦克斯韦-泊松分布，开发了一种灵活的治愈率生存模型。该模型包括文献中讨论的一些著名治愈率模型作为特殊情况。从癌症临床试验中获得的数据通常是右删失的，在这种情况下，可以使用期望最大化算法基于右删失数据有效地估计模型参数。在本文中，我们考虑竞争原因情况，并假设事件发生时间遵循威布尔分布，我们推导了用于估计不同治愈率生存模型参数的期望最大化算法的必要步骤。通过对观测信息矩阵求逆来获得最大似然估计的标准误差。通过广泛的蒙特卡罗模拟研究来检验这里开发的推断方法。最后，我们用关于癌症复发的真实数据说明了所提出的方法。
