The work proposes a new family of survival models called the Odd log-logistic generalized Neyman type A long-term. We consider different activation schemes in which the number of factors M has the Neyman type A distribution and the time of occurrence of an event follows the odd log-logistic generalized family. The parameters are estimated by the classical and Bayesian methods. We investigate the mean estimates, biases, and root mean square errors in different activation schemes using Monte Carlo simulations. The residual analysis via the frequentist approach is used to verify the model assumptions. We illustrate the applicability of the proposed model for patients with gastric adenocarcinoma. The choice of the adenocarcinoma data is because the disease is responsible for most cases of stomach tumors. The estimated cured proportion of patients under chemoradiotherapy is higher compared to patients undergoing only surgery. The estimated hazard function for the chemoradiotherapy level tends to decrease when the time increases. More information about the data is addressed in the application section.

这项工作提出了一个新的生存模型家族，称为 Odd log-logistic 广义 Neyman type A long-term。我们考虑不同的激活方案，其中因子的数量M具有 Neyman A 型分布，并且事件发生的时间遵循奇数对数逻辑广义家庭。通过经典和贝叶斯方法估计参数。我们使用蒙特卡罗模拟研究了不同激活方案中的平均估计、偏差和均方根误差。通过频率论方法的残差分析用于验证模型假设。我们说明了所提出的模型对胃腺癌患者的适用性。选择腺癌数据是因为该疾病是大多数胃肿瘤病例的原因。与仅接受手术的患者相比，接受放化疗的患者的估计治愈比例更高。随着时间的增加，放化疗水平的估计风险函数趋于降低。