Baghestani Ahmad Reza, Zayeri Farid, Akbari Mohammad Esmaeil, Shojaee Leyla, Khadembashi Naghmeh, Shahmirzalou Parviz
Researcher, Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran E-mail :
Asian Pac J Cancer Prev. 2015;16(17):7923-7. doi: 10.7314/apjcp.2015.16.17.7923.
The Cox PH model is one of the most significant statistical models in studying survival of patients. But, in the case of patients with long-term survival, it may not be the most appropriate. In such cases, a cure rate model seems more suitable. The purpose of this study was to determine clinical factors associated with cure rate of patients with breast cancer.
In order to find factors affecting cure rate (response), a non-mixed cure rate model with negative binomial distribution for latent variable was used. Variables selected were recurrence cancer, status for HER2, estrogen receptor (ER) and progesterone receptor (PR), size of tumor, grade of cancer, stage of cancer, type of surgery, age at the diagnosis time and number of removed positive lymph nodes. All analyses were performed using PROC MCMC processes in the SAS 9.2 program.
The mean (SD) age of patients was equal to 48.9 (11.1) months. For these patients, 1, 5 and 10-year survival rates were 95, 79 and 50 percent respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve showed cure model's use competence.
Unlike other variables, existence of ER and PR positivity will increase probability of cure in patients. In the present study, Weibull distribution was used for the purpose of analysing survival times. Model fitness with other distributions such as log-N and log-logistic and other distributions for latent variable is recommended.
Cox PH模型是研究患者生存情况的最重要统计模型之一。但是，对于长期生存的患者，它可能不是最合适的。在这种情况下，治愈率模型似乎更合适。本研究的目的是确定与乳腺癌患者治愈率相关的临床因素。
为了找到影响治愈率（反应）的因素，使用了一种具有负二项分布的潜在变量的非混合治愈率模型。选择的变量包括复发性癌症、HER2状态、雌激素受体（ER）和孕激素受体（PR）、肿瘤大小、癌症分级、癌症分期、手术类型、诊断时的年龄以及切除的阳性淋巴结数量。所有分析均使用SAS 9.2程序中的PROC MCMC过程进行。
患者的平均（标准差）年龄为48.9（11.1）岁。对于这些患者，1年、5年和10年生存率分别为95%、79%和50%。所有上述变量在治愈比例方面均有影响。Kaplan-Meier曲线显示了治愈模型的适用性。
与其他变量不同，ER和PR阳性的存在会增加患者治愈的概率。在本研究中，使用威布尔分布来分析生存时间。建议使用其他分布（如对数正态分布和对数逻辑分布）以及其他潜在变量分布来进行模型拟合。
