Department of Statistics, Sun Yat-sen University, Guangzhou, China.
Stat Methods Med Res. 2019 Jul;28(7):2150-2164. doi: 10.1177/0962280217751520. Epub 2018 Jan 15.
Respiratory cancer is one of the most commonly diagnosed cancers as well as the leading cause of cancer death. Numerous efforts have been devoted to reducing the death rate of respiratory cancer. In this article, we propose a semi-parametric Cox model with latent variables to assess the effects of observed and latent risk factors on survival time of respiratory cancer. The characteristics of latent risk factors are characterized via multiple observed indicators by a confirmatory factor analysis model. We develop a Bayesian estimation procedure to obtain the estimates of parameters. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method is applied to analyze the Surveillance, Epidemiology, and End Results Program data set.
肺癌是最常见的癌症之一，也是癌症死亡的主要原因。为了降低肺癌的死亡率，人们做出了许多努力。在本文中，我们提出了一种带有潜在变量的半参数 Cox 模型，以评估观察到的和潜在的危险因素对肺癌患者生存时间的影响。潜在危险因素的特征通过验证性因子分析模型，由多个观察指标来描述。我们开发了一种贝叶斯估计程序来获得参数的估计值。模拟结果表明，所提出方法的性能令人满意。该方法应用于分析监测、流行病学和最终结果计划数据集。
